Training & Workshop Sessions
– Taught by World-Class Data Scientists –
Learn the latest data science concepts, tools, and techniques from the best. Forge a connection with these rock stars from industry and academia, who are passionate about molding the next generation of data scientists.
ODSC EUROPE Hybrid Conference 2023 | June
Register your interest
Beginner to Advanced Level Training
From the Leading Instructors in the Industry
Machine Learning
Federated Learning for Data Privacy
Explainable AI and Bias in machine learning
Machine Learning at Scale using Apache Spark
Automated Machine Learning (AutoML)
Causal Inference with Machine Learning
Deep Learning
Deep Learning with PyTorch & Tensorflow
Introduction to Deep learning
Deepfakes Tutorial
Deep Reinforcement learning
NLP
Transfer Learning in NLP
NLP Pre-trained Transformer Models with Bert, Ernie,, and GPT-2
Hugging Face Transformer Library Workshop
Applications of NLP; Sentiment Analysis, Dialog Systems, and Semantic Search
Advanced Topics in NLP
ADDITIONAL TUTORIALS & WORKSHOPS
Machine Learninng for Stock Trading
An Introduction to Macine Learing for Finance
Quantitative Finance: Enhancement with Vector Search
Optimizing Healthcare with Vector Search
Beyond the Basics: Data Visualization in Python
Communicating Data Insights with Impact
Some of Our Featured Europe Instructors
Click on the toggle bars for information around sessions and instructors. Check full Speaker and instructor line-up here.
Laurence Moroney
Laurence Moroney leads AI Advocacy at Google, working with the Google AI Research and product development teams. He’s the best-selling author of ‘AI and Machine Learning for Coders,’ as well as the instructor on the Fundamentals of TinyML course at HarvardX, and the popular TensorFlow specializations with deeplearning.ai and Coursera. He’s passionate about empowering software developers to succeed in Machine Learning, democratizing AI as a result. Laurence is based on Washington State in the USA.
A Hands-on Guide to Machine Learning with TensorFlow(Tutorial)
Daria Stepanova, PhD
Daria Stepanova is a lead research scientist at Bosch Center for Artificial Intelligence. Her research interests include knowledge representation and reasoning, machine learning and neuro-symbolic AI. Previously Daria was a senior researcher at Max Plank Institute for Informatics (Germany), where she was heading a group on semantic data. Daria got her PhD in Computational Logic from Vienna University of Technology (Austria) in 2015. Before starting her PhD she worked as a visiting researcher at the School of Computing Science at Newcastle University (UK) in an industrially-oriented project.
Rule Induction and Reasoning in Knowledge Graphs(Tutorial)
Dr. Yves J. Hilpisch
Dr. Yves J. Hilpisch is founder and CEO of The Python Quants (http://tpq.io), a group focusing on the use of open source technologies for financial data science, artificial intelligence, algorithmic trading, and computational finance. He is also founder and CEO of The AI Machine (http://aimachine.io), a company focused on AI-powered algorithmic trading based on a proprietary strategy execution platform.
Yves has a Diploma in Business Administration, a Ph.D. in Mathematical Finance and is Adjunct Professor for Computational Finance at Miami Herbert Business School.
Karin Wolok
Karin is currently the leading developer community programming in the Developer Relations team at StarTree. Karin initially began her career in entertainment marketing working with the likes of names like Eminem and Live Nation. She also launched a successful professional women’s network in two major cities in the U.S., organized events for her local Data Science meetup, and helped lead a on-going hackathon to put machine learning in the hands of cancer biologists. Her journey working in data eventually let her to a position as Program Manager for Community Development for the leading graph database in the world, Neo4j. Most recently, she was brought on to StarTree to improve the adoption and success of the overall developer community.
Real-Time Analytics: Going Beyond Stream Processing with Apache Pinot(Workshop)
Brian Lucena, PhD
Brian Lucena is Principal at Numeristical, where he advises companies of all sizes on how to apply modern machine learning techniques to solve real-world problems with data. He is the creator of three Python packages: StructureBoost, ML-Insights, and SplineCalib. In previous roles he has served as Principal Data Scientist at Clover Health, Senior VP of Analytics at PCCI, and Chief Mathematician at Guardian Analytics. He has taught at numerous institutions including UC-Berkeley, Brown, USF, and the Metis Data Science Bootcamp.
StructureBoost: Gradient Boosting with Categorical Structure(Workshop)
Christian Leibig, PhD
Christian Leibig is Director of Machine Learning at Vara, leading the development of methods from research to production. He obtained a Ph.D. in Neural Information Processing from the International Max Planck Research School in Tübingen and a diploma in physics from the University of Konstanz. Before joining Vara, he worked as a Postdoctoral Researcher at the University Clinics in Tübingen on the applicability of Bayesian Deep Learning and machine learning applications for the healthcare space for ZEISS and held research and internship positions with Max Planck, LMU Munich and the Natural and Medical Sciences Institute in Reutlingen. The method and software of his PhD work, an unsupervised solution for neural spike sorting from HDCMOS-MEA data is distributed by Multichannel Systems (Harvard Bioscience). His work on applying and assessing uncertainty methods to large scale medical imaging was among the first in the field and awarded with key note speaker invitations. He enjoys all of theory, software engineering, and people management, in particular for applications that have a meaningful impact, such as diagnosing cancer early.
Towards a Scalable Deployment of AI Models via Uncertainty Quantification(Workshop)
Duygu Altinok, PhD
Duygu Altinok is a senior NLP engineer with 12 years of experience in almost all areas of NLP including search engine technology, speech recognition, text analytics and conversational AI. She authored several publications in NLP area at conferences such as LREC and CLNLP. She also enjoys working for open-source projects and a contributor of spaCy library.
Duygu earned her undergraduate degree in Computer Engineering from METU, Ankara in 2010 and later earned her Master’s degree in Mathematics from Bilkent University, Ankara in 2012. She spent 2 years at University of Bonn for her PhD studies. She is currently a senior engineer at Deepgram with a focus on conversational AI and speech technology.
Originally from Istanbul, Duygu currently resides in Berlin, DE with her cute dog Adele.
Sentiment Analysis Tricks with Keras, spaCy and Transformers(Tutorial)
Daniel Voigt Godoy
Daniel has been teaching machine learning and distributed computing technologies at Data Science Retreat, the longest-running Berlin-based bootcamp, for more than three years, helping more than 150 students advance their careers. He writes regularly for Towards Data Science. His blog post “Understanding PyTorch with an example: a step-by-step tutorial” reached more than 220,000 views since it was published. The positive feedback from the readers motivated him to write the book Deep Learning with PyTorch Step-by-Step, which covers a broader range of topics.
Daniel is also the main contributor of two python packages: HandySpark and DeepReplay. His professional background includes 20 years of experience working for companies in several industries: banking, government, fintech, retail and mobility.
Julia Lintern
Julia Lintern currently works as an instructor for the Metis Data Science Flex Program. Previously, she worked as a Data Scientist for the New York Times. Julia began her career as a structures engineer designing repairs for damaged aircraft. Julia holds an MA in applied math from Hunter College, where she focused on visualizations of various numerical methods and discovered a deep appreciation for the combination of mathematics and visualizations. During certain seasons of her career, she has also worked on creative side projects such as Lia Lintern, her own fashion label.
Introduction to Machine Learning (Bootcamp)
Oliver Zeigermann
Oliver is a software developer from Hamburg Germany and has been a practitioner for more than 3 decades. He specializes in frontend development and machine learning. He is the author of many video courses and textbooks.
Image Recognition with OpenCV and TensorFlow(Training)
Leonidas Souliotis, PhD
Leonidas (Leo) is a Senior Data Scientist at Astrazeneca. His work is focused around machine learning in oncology, including clinical and non clinical applications. He is also enthusiastic about NLP applications in oncology and how this can be used to leverage patient treatment. He is also a workshop facilitator in the European Leadership University (ELU), NL and has also been a data science educator at DataCamp. He holds a PhD from the University of Warwick, UK. in bioinformatics and ML, an MSc in statistics from Imperial College London, UK and a BSc in Statistics and Insurance Science from the University of Piraeus, GR.
Introduction to Python for Data Analysis(Bootcamp)
Sonam Srivastava
Sonam Srivastava is the founder of Wright Research, an India-based Robo-advisor, where she creates data-driven portfolios out of her deep passion for quant finance. Wright Research is a wealth creator in the digital space that uses scientific data-driven methods to tactically extract opportunities across assets in the public markets to grow clients’ wealth. Wright functions as SEBI registered Robo advisor and is among the most popular advisors among millennial investors with more than 30000 clients and 125 crore+ in assets. Wright Research has delivered a 90% + outperformance over the index in the last 2.5 years. She has 10+ years of experience in investment research and portfolio management, working on systematic strategies, long-short strategies, and algorithmic trading. She started her career in the field with Mumbai-based Forefront Capital, which got acquired by Edelweiss. At Edelweiss, she worked as an algorithm designer at Edelweiss’s institutional equity broking desk. After that, she worked at HSBC Europe as a quant building factor-driven portfolio solutions. Before starting Wright Research, she also worked at Qplum, doing portfolio management at the artificial intelligence-driven Robo-advisor. She graduated from IIT Kanpur and has a master’s in financial engineering from Worldquant University. She is a globally recognized researcher and works as a visiting faculty as AI in Finance Institute New York and BSE Institute Limited.
Deep Reinforcement Learning for Asset Allocation in US Equities (Tutorial)
Jacob Tomlinson
Jacob Tomlinson is a senior Python software engineer at NVIDIA with a focus on deployment tooling for distributed systems. His work involves maintaining open source projects including RAPIDS and Dask. RAPIDS is a suite of GPU accelerated open source Python tools which mimic APIs from the PyData stack including those of Numpy, Pandas and SciKit-Learn. Dask provides advanced parallelism for analytics with out-of-core computation, lazy evaluation and distributed execution of the PyData stack. He also tinkers with the open source chatbot automation framework Opsdroid in his spare time. Jacob volunteers with the local tech community group Tech Exeter and lives in Exeter, UK.
GPU Development with Python 101(Workshop)
Adrin Jalali, PhD
He is a computer scientist / bioinformatician who has turned to be a core developer of `scikit-learn` and `fairlearn`, and work as a Machine Learning Engineer at Hugging Face. He is also an organizer of PyData Berlin.
These days he mostly focus on aspects of machine learning and tools which help with creating more ethical and fair decision making systems. This trend has influenced him to work on `fairlearn`, and to work on aspects of `scikit-learn` which would help tools such as `fairlearn` to work more fluently with the package; and at Hugging Face, his focus is to enable the community of these libraries to be able to share their models more easily and be more open about their work.
Hugo Bowne-Anderson, PhD
Hugo Bowne-Anderson is a data scientist, writer, educator & podcaster. His interests include promoting data & AI literacy/fluency, helping to spread data skills through organizations and society and doing amateur stand up comedy in NYC. He does many of these at DataCamp, a data science training company educating over 3 million learners worldwide through interactive courses on the use of Python, R, SQL, Git, Bash and Spreadsheets in a data science context. He has spearheaded the development of over 25 courses in DataCamp’s Python curriculum, impacting over 170,000 learners worldwide through my own courses. He hosts and produce the data science podcast DataFramed, in which he uses long-format interviews with working data scientists to delve into what actually happens in the space and what impact it can and does have. He earned PhD in Mathematics from the University of New South Wales, Australia and has conducted biomedical research at the Max Planck Institute in Germany and Yale University, New Haven.
Full-stack Machine Learning for Data Scientists(Tutorial)
Ville Tuulos
Ville has been developing infrastructure for machine learning for over two decades. He has worked as an ML researcher in academia and as a leader at a number of companies, including Netflix where he led the ML infrastructure team that created Metaflow, a popular open-source framework for data science infrastructure. He is a co-founder and CEO of Outerbounds, a company developing modern human-centric ML. He is also the author of an upcoming book, Effective Data Science Infrastructure, published by Manning.
Human-Friendly, Production-Ready Data Science with Metaflow(Talk)
Alex Peattie
Alex Peattie is the co-founder and CTO of Peg, a technology platform helping multinational brands and agencies to find and work with top YouTubers. Peg is used by over 1500 organisations worldwide including Coca-Cola, L’Oreal and Google.
An experienced digital entrepreneur, Alex spent six years as a developer and consultant for the likes of Grubwithus, Huckberry, UNICEF and Nike, before joining coding bootcamp Makers Academy as senior coach, where he trained hundreds of junior developers. Alex was also a technical judge at this year’s TechCrunch Disrupt conference.
Hearing is Believing: Generating Realistic Speech with Deep Learning(Workshop)
Leonardo De Marchi
Leonardo De Marchi holds a Master in Artificial intelligence and has worked as a Data Scientist in the sports world, with clients such as the New York Knicks and Manchester United, and with large social networks, like Justgiving. His previous experience includes Head of Data Science and Analytics in Bumble, the largest dating site with over 500 million users, heading the team through an acquisition and an IPO. He is also the lead instructor at ideai.io, a company specialized in Reinforcement Learning, Deep Learning and Machine Learning training. He is also a contractor for several companies and for the European Commission, as an expert in AI and Machine Learning. As an author he wrote “Hands On Deep Learning” and he authored an online training course for O’Reilly, Introduction to Reinforcement Learning. In the academic world, he also helped set up the PhD center on Interactive Artificial Intelligence and will take part in the Inner Assessment Board to assign funding to Irish research in AI.
Guglielmo Iozzia
Guglielmo is a Biomedical Engineer with an extensive background in Software Engineering and Data Science applied to different contexts, such as Biotech Manufacturing, Healthcare and DevOps, just to mention the latest, and a lifelong learner. Currently busy unlocking business value through Deep Learning projects, mostly in Computer Vision (not restricted to this field by the way).
He has been recognized as DataOps Champion at the Streamsets DataOps Summit 2019 and awarded as one of the Top 50 Tech Visionaries at the 2019 Dubai Intercon Conference.
He is also an international speaker and author of the following book: Hands-on Deep Learning with Apache Spark @Packt https://www.packtpub.com/big-data-and-business-intelligence/hands-deep-learning-apache-spark
Not Just Deep Fakes: Applications of Visual Generative Models in Pharma Manufacturing(Tutorial)
Hadrien Jean, PhD
Hadrien Jean is a machine learning scientist working at My Medical Assistent where he is developing deep learning models in the medical domain. He wrote the book Essential Math for Data Science (https://www.essentialmathfordatascience.com/) aimed at helping people to get the math needed in data science from a coding perspective. He previously worked at Ava on speech diarization. He also worked on a bird detection project using deep learning. He completed his Ph.D. in cognitive science at the École Normale Supérieure (Paris, France) on the topic of auditory perceptual learning with a behavioral and electrophysiological approach. He has published a series of blog articles aiming at building intuition on mathematics through code and visualization (https://hadrienj.github.io/posts/).
Introduction to Linear Algebra for Data Science and Machine Learning With Python(Bootcamp)
Corey Weisinger
Corey studied Mathematics at Michigan State University and works as a Data Scientist with KNIME where he focuses on Time Series Analysis, Forecasting, and Signal Analytics. He is the creator and instructor of the KNIME Time Series Analysis course, author of the e-book: Alteryx to KNIME, creator of the KNIME Time Series Analysis components, and Co-Author of the upcoming Codeless Time Series Analysis Book with Packt.
Moving into the Frequency Domain with the Fourier Transform(Tutorial)
Johnathan Azaria
Bio Coming Soon!
Gal Naamani
Gal Naamani has been working as a data scientist for 4 years, with the past 3 years being at Fiverr. As the Senior Data Scientist, Gal works closely with developers, analysts, product managers, and business owners on growth opportunities and new ideas, from research to production. Gal currently has leading roles in projects that are focused around search engine ranking, promoted ads, online bidding optimization, exploration-exploitation problems, monitoring, and more.
Utilizing Advanced Monitoring Capabilities to Promote Product-oriented Data Science(Tutorial)
Carl Osipov
Carl implemented his first neural net in 2000. He is a senior director of the AI / ML practice at Cognizant, focusing on communications, technology, and media customers. Previously he worked on deep learning and machine learning at Google and IBM. Carl is an author of over 20 articles in professional, trade, and academic journals, an inventor with 6 patents at USPTO, and holds 3 corporate awards from IBM for his innovative work. His machine learning book, “MLOps Engineering at Scale” continues to receive reader acclaim. You can find out more about Carl from his blog www.cloudswithcarl.com
Revealing the Inner Self: Automatic Differentiation (Autodiff) Clearly Explained(Workshop)
Jason Mayes
Jason is the public face of TensorFlow.js, helping web engineers globally take their first steps with machine learning in JavaScript. He also combines his knowledge of the technical and creative worlds to develop innovative prototypes for Google’s largest customers and internal teams with over 15 years experience working within web engineering and investigating emerging technologies.
He holds an MEng in Computer Science, is a member of the British Computing Society, and is a certified information privacy technologist. Jason loves sharing knowledge online which has attracted a global following. In his spare time he can be found walking the wings of flying aircraft being one of the few people in the world who has been trained in the art of wing walking.
ODSC Newsletter
Stay current with the latest news and updates in open source data science. In addition, we’ll inform you about our many upcoming Virtual and in person events in Boston, NYC, Sao Paulo, San Francisco, and London. And keep a lookout for special discount codes, only available to our newsletter subscribers!
Half-Day Training | Deep Learning | Intermediate-Advanced
In this session, participants will be introduced to recent advances in audio-visual speech enhancement and separation, which has a variety of different applications…more details
Prof. Zheng-Hua Tan is a Professor of Machine Learning and Speech Processing, a Co-Head of the Centre for Acoustic Signal Processing Research (CASPR), and Machine Learning Research Group Leader in the Department of Electronic Systems at Aalborg University, Denmark. Prof. Zheng-Hua Tan was a Visiting Scientist/Professor at the Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology (MIT), Cambridge, USA, an Associate Professor in the Department of Electronic Engineering at Shanghai Jiao Tong University, China, and a postdoctoral fellow at AI Spoken Language Lab, in the Department of Computer Science at KAIST, Korea. He received the B.S. and M.S. degrees in electrical engineering from Hunan University, China, in 1990 and 1996, respectively, and the Ph.D. degree in electronic engineering from Shanghai Jiao Tong University, China, in 1999. His research interests include machine learning, deep learning, pattern recognition, speech and speaker recognition, noise-robust speech processing, multimodal signal processing, and social robotics. He has over 200 publications. He edited the book Automatic Speech Recognition on Mobile Devices and over Communication Networks (Springer, 2008). He is the elected Chair of the IEEE Machine Learning for Signal Processing Technical Committee. He is an Associate Editor for the IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH AND LANGUAGE PROCESSING. He has served as an Editorial Board Member/Associate Editor for several journals including Computer Speech and Language, and Digital Signal Processing. He was a Lead Guest Editor of the IEEE Journal of STSP and a Guest Editor of several journals including Neurocomputing. He was the General Chair of IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP 2018), Aalborg, Denmark, and was a Program Co-Chair for IEEE Workshop on Spoken Language Technology (SLT 2016), San Diego, California, USA. He has served as a Chair, Program Co-chair, Area and Session Chair, and Tutorial Speaker of many international conferences. He is a Senior Member of the IEEE.
Daniel Michelsanti is an Industrial Postdoctoral Researcher at Demant and Aalborg University. He has a PhD in Electrical and Electronic Engineering obtained at Aalborg University. Currently, he is investigating cutting-edge technologies for next-generation hearing assistive devices, with the goal of improving the life quality of people with hearing loss.
Full-Day Training | Machine Learning | Beginner-Intermediate
This session is a hands-on introduction to Machine Learning in Python with scikit-learn. You will learn to build and evaluate predictive models on tabular data using the main tools of the Python data-science stack (Jupyter, numpy, pandas, matplotlib and scikit-learn)…more details
Olivier Grisel is a machine learning engineer at Inria. He is a member of the team of maintainers of the scikit-learn project. Scikit-learn is an Open Source machine learning library written in Python. His work is supported by the Fondation Inria and its partners
Full-Day Training | NLP | Deep Learning | Intermediate-Advanced
Being specialized in domains like computer vision and natural language processing is no longer a luxury but a necessity which is expected of any datascientist in today’s fast-paced world! With a hands-on and interactive approach, we will understand essential concepts in NLP along with extensive hands-on examples to master state-of-the-art tools, techniques and methodologies for actually applying NLP to solve real- world problems. We will leverage machine learning, deep learning and deep transfer learning to learn and solve popular tasks using NLP including NER, Classification, Recommendation \ Information Retrieval, Summarization, Classification, Language Translation, Q&A and Topic Models…more details
Anuj Gupta is a head the Machine Learning and Data Science teams at Vahan. Prior to this, he was heading ML efforts for Intuit, Huawei Technologies, Freshworks, Chennai and Airwoot, Delhi. He did his masters in theoretical computer science from IIIT Hyderabad and he dropped out of his Phd from IIT Delhi to work with startups.
He is a regular speaker at ML conferences like Pydata, Nvidia forums, Fifth Elephant, Anthill. He has also conducted a bunch of workshop attended by machine learning practitioners. He is also the co-organizer for one of the early Deep Learning meetups in Bangalore. He is also Editor of “”Anthill-2018″” – deep learning focused conference by HasGeek.
Half-Day Training | Machine Learning | Beginner
The objective of the session is to provide some basic understanding of Python as a language to be used for data processing. Python syntax is very readable and easy to work with, and its rich ecosystem of libraries makes it one of the most popular programming languages in the World.
We will see some common tools and characteristics of Python that are basic to analyse data, like how to import data from files and to generate results in multiple formats. We will also see some ways to speed-up the processing of data.
This workshop is aimed at people with little to no knowledge of Python, though some programming knowledge is required, even if it’s in a different language…more details
Jaime Buelta has been a professional programmer since 2002 and a full-time Python developer since 2010. He has developed software for a variety of fields, focusing, in the last 10 years, on developing web services in Python in the gaming and finance industries. He is a strong proponent of automating everything to make computers do most of the heavy lifting, so humans can focus on the important stuff. He published his first book, “Python Automation Cookbook”, in 2018 (now updated recently with an extended second edition), followed by “Hands-On Docker for Microservices with Python” the following year. He is currently working as Software Architect in Double Yard in Dublin, Ireland, and is a regular speaker at PyCon Ireland.
Half-Day Training | Quant Finance | Machine Learning | Intermediate
The rapid progress in machine learning (ML) and the massive increase in data availability has enabled novel approaches to quantitative investment and increased the demand for the application of data science to develop discretionary and automated trading strategies.
This workshop covers popular ML use cases for the investment industry. In particular, it focuses on how ML fits into the workflow of developing a trading strategy, from the engineering of financial features to the development of an ML model that generates tradable signals, the backtesting of a trading strategy that acts on these signals and the evaluation of its performance.
We’ll use common Python data science and ML libraries as well as Zipline, Pyfolio, and Alphalens. The code examples will be presented using jupyter notebooks and are based on the second edition of my book ‘Machine Learning for Algorithmic Trading’…more details
Stefan is the founder and Lead Data Scientist at Applied AI. He advises Fortune 500 companies, investment firms and startups across industries on data & AI strategy, building data science teams, and developing machine learning solutions. Before his current venture, he was a partner and managing director at an international investment firm where he built the predictive analytics and investment research practice. He also was a senior executive at a global fintech company with operations in 15 markets.
Earlier, he advised Central Banks in emerging markets, worked for the World Bank, raised $35m from the Gates Foundation to cofound the Alliance for Financial Inclusion, and has worked in six languages across Asia, Africa, and Latin America. Stefan holds Master degrees in Computer Science from Georgia Tech and in Economics from Harvard and Free University Berlin and is a CFA Charterholder. He is the author for ‘Machine Learning for Algorithmic Trading’ and has been teaching data science at Datacamp and General Assembly.
Half-Day Training | Big Data | Beginner
This tutorial will introduce you to the wonderful world of Bayesian data science through the lens of probabilistic programming in Python. In the first half of the tutorial, we will introduce the key concepts of probability distributions via hacker statistics, hands-on simulation, and telling stories of the data-generation processes. We will also cover the basics of joint and conditional probability, Bayes’ rule, and Bayesian inference, all through hands-on coding and real-world examples. In the second half of the tutorial, we will use a series of models to build your familiarity with PyMC3, showcasing how to perform the foundational inference tasks of parameter estimation, group comparison (for example, A/B tests and hypothesis testing), and arbitrary curve regression…more details
Hugo Bowne-Anderson is a data scientist, writer, educator & podcaster. His interests include promoting data & AI literacy/fluency, helping to spread data skills through organizations and society and doing amateur stand up comedy in NYC. He does many of these at DataCamp, a data science training company educating over 3 million learners worldwide through interactive courses on the use of Python, R, SQL, Git, Bash and Spreadsheets in a data science context. He has spearheaded the development of over 25 courses in DataCamp’s Python curriculum, impacting over 170,000 learners worldwide through my own courses. He hosts and produce the data science podcast DataFramed, in which he uses long-format interviews with working data scientists to delve into what actually happens in the space and what impact it can and does have. He earned PhD in Mathematics from the University of New South Wales, Australia and has conducted biomedical research at the Max Planck Institute in Germany and Yale University, New Haven.
Workshop | Big Data | All Levels
In many data science ecosystems data frame is a pivotal object. It is not only very useful conceptually, but also ensures that data transformation operations can be performed efficiently. Therefore packages like data.table in R or pandas in Python are star players.
With the Julia language the situation is different because it gives you the speed out of the box. Therefore the DataFrames.jl package is designed to be a sidekick that conveniently supports your core data analysis pipeline. It has a more focused functionality than e.g. pandas, but at the same time it seamlessly integrates with the whole Julia data science ecosystem.
During this workshop, using hands-on examples, I will discuss the design principles behind DataFrames.jl and walk you through key functionalities provided by this package. All presented materials will be made available before the workshop in a blog post on https://bkamins.github.io/…more details
Bogumił Kamiński is Head of Decision Analysis and Support Unit at Warsaw School of Economics, Poland, and Adjunct Professor at Data Science Laboratory, Ryerson University, Canada. His research interests are techniques of large scale mathematical modelling of complex systems combining simulation, optimization, and machine learning. A particular area of his expertise are agent based simulation and modeling and analysis of complex networks.
Bogumił is one of the core developers of DataFrames.jl package for data wrangling in the Julia language. He is also a top answerer for the [julia] tag on StackOverflow and regularly discusses a wide range of data science related topics on his blog https://bkamins.github.io/.
Tutorial | Machine Learning | Intermediate-Advanced
Advances in information extraction have enabled the automatic construction of large knowledge graphs (KGs) like DBpedia, YAGO, Wikidata of Google Knowledge Graph. Learning rules from KGs is a crucial task for KG completion, cleaning and curation. This tutorial presents state-of-the-art rule induction methods, recent advances, research opportunities as well as open challenges along this avenue...more details
Daria Stepanova is a lead research scientist at Bosch Center for Artificial Intelligence. Her research interests include knowledge representation and reasoning, machine learning and neuro-symbolic AI. Previously Daria was a senior researcher at Max Plank Institute for Informatics (Germany), where she was heading a group on semantic data. Daria got her PhD in Computational Logic from Vienna University of Technology (Austria) in 2015. Before starting her PhD she worked as a visiting researcher at the School of Computing Science at Newcastle University (UK) in an industrially-oriented project.
Tutorials | Machine Learning | Intermediate
Automated machine learning is the science of building machine learning models in a data-driven, efficient, and objective way. It replaces manual trial-and-error with automated, guided processes. In this tutorial, we will guide you through the current state of the art in hyperparameter optimization, pipeline construction, and neural architecture search. We will discuss model-free blackbox optimization methods, Bayesian optimization, as well as evolutionary and other techniques. We will also pay attention to meta-learning, i.e. learning how to build machine learning models based on prior experience. Moreover, we will give practical guidance on how to do meta-learning with OpenML, an online platform for sharing and reusing machine learning experiments, and how to perform automated pipeline construction with GAMA, a novel, research-oriented AutoML tool in Python…more details
Joaquin Vanschoren is Assistant Professor in Machine Learning at the Eindhoven University of Technology. His research focuses on machine learning, meta-learning, and understanding and automating learning. He founded and leads OpenML.org, an open science platform for machine learning. He received several demo and open data awards, has been tutorial speaker at NeurIPS and ECMLPKDD, and invited speaker at ECDA, StatComp, AutoML@ICML, CiML@NIPS, DEEM@SIGMOD, AutoML@PRICAI, MLOSS@NIPS, and many other occasions. He was general chair at LION 2016, program chair of Discovery Science 2018, demo chair at ECMLPKDD 2013, and he co-organizes the AutoML and meta-learning workshop series at NIPS and ICML. He is also co-editor of the book ’Automatic Machine Learning: Methods, Systems, Challenges’.
Pieter Gijsbers is a Ph.D. student at the Eindhoven University of Technology. His areas of interest are automated machine learning and meta-learning. He is the main author of GAMA, a research-focused open-source AutoML tool. While GAMA is easy to use for end-users, it also allows researchers to try different search techniques and visualize the optimization process. He is a co-author of the Open Source AutoML Benchmark. He is also part of the team working on the openml-python package, providing an easy to use Python interface to OpenML.
Workshop | Deep Learning | Intermediate-Advanced
CNNs, specialized neural networks for Computer Vision tasks, are used in sensitive contexts and exposed in the wild. While extremely accurate, they are also sensitive to imperceptible perturbations that can’t be detected by human eyes. For this reason, they have been targeted by hackers which implemented AI-based techniques for their malicious purposes. During this workshop we are going to learn some synthetic attacking techniques and a defence strategy to mitigate the effect of such attacks and make neural networks more robust to them, while at the same time keeping minimal impact on the accuracy of the model and implementation costs. We would also try to understand if Transformers applied to Computer Vision tasks are immune to Adversarial Attacks…more details
Guglielmo is a Biomedical Engineer with an extensive background in Software Engineering and Data Science applied to different contexts, such as Biotech Manufacturing, Healthcare and DevOps, just to mention the latest, and a lifelong learner.
Currently busy unlocking business value through Deep Learning projects, mostly in Computer Vision (not restricted to this field by the way).
He has been recognized as DataOps Champion at the Streamsets DataOps Summit 2019 and awarded as one of the Top 50 Tech Visionaries at the 2019 Dubai Intercon Conference.
He is also an international speaker and author of the following book:
Hands-on Deep Learning with Apache Spark @Packt https://www.packtpub.com/big-data-and-business-intelligence/hands-deep-learning-apache-spark
Workshop | Deep Learning | Intermediate
Learn the basics of building a PyTorch model using a structured, incremental and from first principles approach. Find out why PyTorch is the fastest growing Deep Learning framework and how to make use of its capabilities: autograd, dynamic computation graph, model classes, data loaders and more.
The main goal of this session is to show you how PyTorch works: we will start with a simple and familiar example in Numpy and “torch” it! At the end of it, you should be able to understand PyTorch’s key components and how to assemble them together into a working model.
We will use Google Colab and work our way together into building a complete model in PyTorch. You should be comfortable using Jupyter notebooks, Numpy and, preferably, object oriented programming…more details
Daniel has been teaching machine learning and distributed computing technologies at Data Science Retreat, the longest-running Berlin-based bootcamp, for more than three years, helping more than 150 students advance their careers.
He writes regularly for Towards Data Science. His blog post “Understanding PyTorch with an example: a step-by-step tutorial” reached more than 220,000 views since it was published.
The positive feedback from the readers motivated him to write the book Deep Learning with PyTorch Step-by-Step, which covers a broader range of topics.
Daniel is also the main contributor of two python packages: HandySpark and DeepReplay.
His professional background includes 20 years of experience working for companies in several industries: banking, government, fintech, retail and mobility.
Workshop | Machine Learning | Beginner-Intermediate
In this session, we will work through the basics of solving a classification-based machine learning problem using python and scikit-learn, and do a comparative study of two popular algorithms…more details
Yamini Rao is a Developer Advocate for IBM. She complies various developer scenarios and training material, including demos, writing blog posts, creating audio-visual artefacts and giving hands-on workshops and training sessions based on IBM Cloud technologies. She works with various developer communities across the UK. As a developer advocate, Yamini is involved in public speaking engagements at conferences and meetups and also organises some of these events as part of the role. The other part of Advocacy involves closing the loop, to collect feedback from the developer community about how they are using the technology and channel this back to the engineering and product management teams. She has a background in computer science and has previously worked as an Implementation Engineer for various IBM Analytical tools.
Tutorial | NLP | Deep Learning | Beginner-Intermediate
Have you wondered what is the technology behind the GPT models? In this talk, we are going to discuss the Transformer neural networks, introduced in 2017…more details
A former theoretical physicist turned machine learning engineer, Olga is now building a smart data annotation platform at Scaleway as a technical product manager. On the community side, she enjoys blogging about the latest advancements in AI both in and out of working hours. Some of her writing can be seen on medium.com/@olgapetrova_92798.
Workshop | Quant Finance | Intermediate-Advanced
This target of this workshop is twofold. On one hand, it is familiarizing attendees with mechanics of reinforcement learning (RL) applied to financial environments. On the other side, it aims to uncover key differences between popular RL applications (as playing video games) and financial ones, ignoring which inevitably will lead to losses of time and capital. With such insights and code boilerplates, attendees will be able to avoid harsh mistakes and implement environment-driven strategies faster…more details
Alex Honchar is a tech entrepreneur and educator. Currently, he is co-founder and ML director at Neurons Lab – a consulting firm specializing in healthcare, finance, and IoT. Also, he writes a popular blog on Medium about machine learning applications and leadership. Previously he worked as an independent consultant with SMBs and startups on rapid go-to-market ML solutions and taught machine learning courses at the University of Verona and Ukrainian Catholic University.
Workshop | Responsible AI | Beginner
This workshop offers a gentle introduction to reproducible and elegantly formatted document generation with R Markdown. R Markdown presents a framework for reproducible workflows. It allows you to use multiple languages including R, Python, and SQL and helps you automate the production of HTML or PDF reports by relying on the power of Pandoc together with Lua-filters.
Participants will learn how to implement literate programming practices to make their workflows fully reproducible and produce automated reports. We will first cover the basics of narrative text and code integration. Then, we focus on working on a template that is fully optimized for two different output formats, HTML and PDF. While in the stage of explorative data analysis and with an eye on content only, our template allows you to produce beautiful HTML reports of your analyses, optimized for the interactive exploration of your data. While in the stage of dissemination and with an eye on the presentation of results, our template allows you to produce beautifully typeset PDF reports, ready to be circulated or published any time…more details
Julia Schulte-Cloos is a Marie Skłodowska-Curie funded LMU Research Fellow at the Geschwister Scholl Institute of Political Science at LMU Munich. Her main research interests lie in comparative politics, political behavior, research methodology, and reproducibility. Julia Schulte-Cloos has earned her PhD from the European University Institute.
Workshop | Machine Learning | All Levels
By completing this workshop, you will develop an understanding of the different freely available RS data sources out there and open-source software tools that can be used for analysing these...more details
I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year’s experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics.
I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing.
Tutorial | Quant Finance | Intermediate
This tutorial explores machine learning applications in economics and finance using TensorFlow 2. It starts by examining how TensorFlow and machine learning can be used to solve empirical and theoretical models in economics…more details
Isaiah Hull is a senior economist in the research division of Sweden’s Central Bank (Sveriges Riksbank). He holds a PhD in economics from Boston College and conducts research on computational economics, machine learning, and quantum computing. He is also the instructor for DataCamp’s “Introduction to TensorFlow in Python” course and the author of “Machine Learning for Economics in Finance in TensorFlow 2.”
Workshop | MLOps | Intermediate-Advanced
Airflow is a leading open-source workflow orchestrator that offers a very wide range of possibilities. It can be integrated with Kubernetes using the KubernetesPodOperator to create pipelines that are extremely customizable. This is what we use to preprocess data and train ML models for PowerOP, Dataswati’s SAAS for optimizing food industry production lines…more details
Luis spent his education and early career in the realm of Transport (Civil Engineering Diploma, MSc Transport from Imperial College London, French Civil Aviation Authority) but he took the train of data science and machine learning in 2014 with Kaggle and Coursera as teachers. He spent about 3 years as a Data Scientist at Quantmetry, a Data Science and AI Consultancy based in Paris where he helped several big companies in multiple sectors develop solutions involving Natural Language Processing, Geospatial data, and Time series. He has recently joined Dataswati to take the lead of the Data Science team. At Dataswati, they develop a SAAS called PowerOP that offers agro-industrial companies easy integration and visualization of their production data as well as explainable and actionable AI services like quality analysis, smart alerting and recipe or settings recommendation.
Workshop | Big Data
By completing this workshop, you will develop an understanding of various vectors of AI risks to companies and be able to improve the governance inside your organization. Additionally, you will get hands-on experience on the problem of biased data and simulate an adversarial attack on a neural network model…more details
Lukas Csoka, working as Head Big Data Foundations in Swiss Re, one of the biggest global reinsurers, combines data science, consultative thinking and business partnering as the main skills used daily in his job to push design and develop new AI products. His MS in software engineering from Slovak University of Technology and MBA in global management from City University of Seattle are supporting him during this journey. His projects include innovative solutions for farmers using satellites, the application of predictive methods to manage costs inside Swiss Re or the analysis of thousands of client meetings from text and the provision of recommendations to Swiss Re’s traders and many others.
Workshop | Machine Learning | Responsible AI | Beginner
In the days where we have autonomous cars, drones, and automated medical diagnostics, we want to learn more about how to interpret the decisions made by the machine learning models. Having such information we are able to debug the models and retrain it in the most efficient way.
This talk is dedicated to managers, developers and data scientists that want to learn how to interpret the decisions made by machine learning models. We explain the difference between white and black box models, the taxonomy of explainable models and approaches to XAI. Knowing XAI methods is especially useful in any regulated company.
We go through the basic methods like the regression methods, decision trees, ensemble methods, and end with more complex methods based on neural networks. In each example, we use a different data set for each example. Finally, we show how to use model agnostic methods to interpret it and the complexity of the interpretability of many neural networks…more details
Karol Przystalski obtained a PhD degree in Computer Science in 2015 at the Jagiellonian University in Cracow. He is the CTO and founder of Codete where he’s leading and mentoring teams as they work with Fortune 500 companies on data science projects. He also built a research lab for machine learning methods and big data solutions at Codete. Karol gives speeches and trainings in data science with a focus on applied machine learning in German, Polish, and English.
Workshop | MLOps
Ivana is a data scientist, passionate about machine learning and artificial intelligence. As part of the Data Science and Strategy competence center at element61, she helps organizations build and grow business with data. Ivana is an engineering professional with a Master’s degree in Artificial Intelligence from KU Leuven.
Workshop | Machine Learning | Beginner
General Data Protection Regulation (GDPR) is now in place. Are you ready to explain your models? This is a hands-on tutorial for beginners. I will demonstrate the use of open-source H2O platform (https://www.h2o.ai/products/h2o/) with both Python and R for automatic and explainable machine learning. Participants will be able to follow and build regression and classification models quickly with H2O AutoML. They will then be able to explain the model outcomes with various methods...more details
Jo-fai (or Joe) has multiple roles (data scientist / evangelist / community manager) at H2O.ai. Since joining the company in 2016, Joe has delivered H2O talks/workshops in 40+ cities around Europe, US, and Asia. Nowadays, he is best known as the H2O #360Selfie guy. He is also the co-organiser of H2O’s EMEA meetup groups including London Artificial Intelligence & Deep Learning – one of the biggest data science communities in the world with more than 11,000 members (https://www.meetup.com/London-Artificial-Intelligence-Deep-Learning/).
Workshop | MLOps | NLP | Intermediate
In this workshop we will explore the concept of MLOps Orchestration and how it can simplify the process of getting data science to production in any environment (multi-cloud, on-prem, hybrid), from the step of data collection and preparation (across real-time / streaming, historic, structured, or unstructured data), through automated model training to model deployment and monitoring. We will demonstrate how to drastically cut down the time and efforts needed to get data science to production. We’ll show how to map a business problem into an automated ML production pipeline and identify the right tools for the job, and ultimately how to run Al models in production at scale to accelerate business value with AI – all using open source technologies. The session will include a live demo and real customer case studies across use cases such as fraud prevention, real-time recommendation engines and NLP…more details
Yaron Haviv is a serial entrepreneur who has been applying his deep technological experience in data, cloud, AI and networking to leading startups and enterprise companies since the late 1990s. As the co-founder and CTO of Iguazio, Yaron drives the strategy for the company’s MLOps platform and led the shift towards the production-first approach to data science and catering to real-time AI use cases. He also initiated and built Nuclio, a leading open source serverless platform with over 4,000 Github stars and MLRun, Iguazio’s open source MLOps orchestration framework. Prior to co-founding Iguazio in 2014, Yaron was the Vice President of Datacenter Solutions at Mellanox (now NVIDIA), where he led technology innovation, software development and solution integrations. He was also the CTO and Vice President of R&D at Voltaire, a high-performance computing, IO and networking company which floated on the NYSE in 2007. Yaron is an active contributor to the CNCF Working Group and was one of the foundation’s first members. He presents at major industry events and writes tech content for leading publications including TheNewStack, Hackernoon, DZone, Towards Data Science and more.
Workshop | MLOps | Intermediate
This session will explore and demonstrate how DataRobot’s MLOps can speed up deployment, monitor drift and accuracy, ensure governance and ongoing model lifecycle management, including how to do automation retraining and have challenger models in production…more details
Asger holds a master of Computer Science and has for many years been leading software development teams as the CTO of several startup companies. Following that he has been helping many of the biggest enterprises around the world becoming successful with monitoring critical application services in production, and today at DataRobot he is focused on bringing some of the best practices from the DevOps world into the new growing field of MLOps, where some of the principles can be reused but where there is also a great need to have AI and Machine Learning specific capabilities to be able to scale in a standardised and governed way.
Qian Zhao is a London-based data scientist at DataRobot who helps fintech, banking, and healthcare customers accelerate their machine learning capabilities by using the DataRobot platform. Before he joined DataRobot, Qian was a data science manager at PwC and Deloitte, where he led a team of data scientists, delivering innovative and cutting-edge AI and machine learning solutions to enterprise businesses. Qian received his Ph.D. in Neuroscience from UCL in 2012.
Pavel Ustinov is a Lead AI Engineer at DataRobot, where he applies his scientific background and more than 15 years’ experience in software engineering to successfully deliver end-to-end Augmented Intelligence solutions driven by the DataRobot platform. He takes care of EMEA region customers. Pavel began his career as a researcher in the nonlinear dynamics of energy conversion systems before moving to the software engineering and machine learning industry. Prior to joining DataRobot, he also spent time at Anaplan, Société Générale CIB, BNP Paribas Securities Services, and ML/DL startups holding senior/leading technical positions and delivering large scale analytical projects. Pavel received his PhD from Orel State Technical University (Orel, Russia) in Automation and Control of Industrial Systems and PhD from University of Reims Champagne-Ardenne (Reims, France) in Computer Science, Automation and Signal Processing. When not working out over technology he enjoys a high altitude mountaineering.
Workshop | Life Sciences & Pharma | All Levels
A real-world scenario of applying ML in genomics will be discussed, where we shall build a predictor of a special type of DNA structures out of DNA sequence information alone…more details
Alex is a principal investigator at the University of Oxford, leading a group focused on integrative computational biology and machine learning in the MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine. In past, he did his undergraduate studies in pharmaceutical sciences with a research focus on quantum/structural chemistry and NMR spectroscopy. He next moved to the University of Cambridge, first obtaining an MPhil degree in computational biology (Department of Applied Mathematics and Theoretical Physics), followed by a PhD in theoretical chemical biology (Department of Chemistry). He then became an interdisciplinary research fellow in computational genomics and epigenetics (Department of Chemistry and Cancer Research UK Cambridge Institute), before joining the University of Oxford. His research aims at combining machine learning, computational biology, computational chemistry, data from experimental genomics and biophysical techniques to reach a new level of precision in biology at both genome and proteome levels.
Workshop | Machine Learning | All Levels
This will be a 90 minute workshop that will walk through how to set up, run and deploy a federated learning project from scratch…more details
At Scaleout, we are solving the data access challenge in AI. We are developing a world-leading solution for federated learning. In federated learning, you distribute the training of machine learning models to the data. You avoid collecting all data in one place.
Daniel is the CEO and co-founder of Scaleout and has a long background as an entrepreneur and leader in deep tech companies. He co-founded Scandinavia’s first personal DNA-testing company in 2008, was CTO at a multinational growing medtech company for 7 years, and then co-founded the first international accelerator for blockchain startups. As CTO and CEO, he has many years of experience in leading deep tech projects and taking them to market.
Workshop | Responsible Ai | Machine Learning | Intermediate-Advanced
Recently, academics as well as policy makers have written many papers, on responsible data science / AI. Moreover, many open-source packages for bias dashboards or tools for `fairness’ have been proposed. This session aims to provide attendees a broad overview as well as the specific technical background to use the available ` fairness’ tools. In addition, a governance framework describing the precise responsibilities of data scientists will be discussed…more details
Ramon van den Akker works as a data scientist at the AI Center of Excellence and the Risk Modelling departments of de Volksbank, a Dutch retail bank located in Utrecht. He also works, as an associate professor, at the econometrics group of Tilburg University. His research interests cover various fields in data science, machine learning, econometrics and statistics and his research findings have been published in leading journal in econometrics and statistics. In his work at de Volksbank, Ramon mainly works on data science projects and the exploration of new opportunities, but also on governance aspects like frameworks for responsible AI & data science and the use of privacy-preserving data analytics.
Daan Knoope works as an AI Engineer at de Volksbank, the Dutch parent company of several banks and mortgage providers. He has a background in Computer Science (MSc) and has specialized in Algorithmic Data Analysis. During his studies, he researched the application of Dynamic Bayesian Networks on practical use cases to help further the development of explainable AI. Currently, he is focusing on developing AI-models for the bank as well as providing fellow AI Engineers the tools they need to efficiently explore data and build production-ready models
Joris Krijger (1991) works as an Ethics & AI specialist at the Dutch bank de Volksbank while also holding a PhD position at the Erasmus University Rotterdam on that topic. He has a background in Economic Psychology (MA), Philosophy (MA) and Film and Literary Studies (BA) and studied in Glasgow, Buenos Aires and Leiden, where he was awarded a national thesis prize in 2017 by the Royal Dutch Society of the Sciences for his graduation thesis on technology ethics and the financial crisis of 2008. He co-founded high-tech startup Condi Food (Rabobank Wijffels Innovation Award 2014) and was involved in various biomedical initiatives related to bacteriophages. He presently works on bridging the gap between principle and practice in AI Ethics by studying the operationalization of ethical principles from an academic and practical perspective and is reviewer for the AI Ethics Journal, Subject Matter Expert for CertNexus’ ‘Certified Ethical Emerging Technologist’ and Editorial Board Member for Springer’s AI and Ethics Journal.
Workshop | Machine Learning | Quant Finance | Beginner
Unlike some of my prior presentations and tutorials that covered both statistical and neural network-based models for time series analysis, this talk will be introductory in nature and will focus on the discussion of a couple of workhorse statistical time series models that are frequently applied to solving time series forecasting problems…more details
Jeffrey is a VP of Data Science, Data Engineering, and Platform Engineering at the Store Associate Technology of Walmart Global Technology. His prior roles include the Chief Data Scientist at AllianceBernstein, a global asset-management firm that managed nearly $700 billion, Vice President and Head of Data Science at Silicon Valley Data Science, and senior leadership position at Charles Schwab Corporation and KPMG. He has also taught econometrics, statistics, and machine learning at UC Berkeley, Cornell, NYU, University of Pennsylvania, and Virginia Tech. Jeffrey is active in the data science community and often speaks at data science conferences and local events. He has many years of experience in applying a wide range of econometric and machine learning techniques to create analytic solutions for financial institutions, businesses, and policy institutions. Jeffrey holds a Ph.D. and an M.A. in Economics from the University of Pennsylvania and a B.S. in Mathematics and Economics from UCLA.
Tutorial | Quant Finance | Beginner-Intermediate
This tutorial aims to demystify ML by uncovering its underlying mathematics and showing how to apply ML methods to real-world financial data…more details
Dr. Hao Ni is an associate professor in financial mathematics at UCL and a Turing fellow at Alan Turing Institute since September 2016. Prior to this Dr. Hao Ni was a visiting postdoctoral researcher at ICERM and Department of Applied Mathematics at Brown University from 2012/09 to 2013/05 and continued her postdoctoral research at the Oxford-Man Institute of Quantitative Finance until 2016. Dr. Hao Ni finished her D.Phil. in mathematics in 2012 under the supervision of Professor Terry Lyons at University of Oxford.
Tutorial | MLOps | All Levels
Machine learning has evolved from the experimenting stage to real-world production systems with a need for automated quality assurance and delivery, reproducibility and deployment consistency…more details
Magda is a full stack developer at Valohai and one of the organizers of the Turku.py Python meetup. She is passionate about open source, open data and MLOps.
Workshop | Deep Learning | Beginner-Intermediate
This workshop will walk you through how to convert mathematical concepts into code to build AI models. At the end of the workshop, you’ll learn how to write code from scratch to do the magic i.e. generate images and deepfake video…more details
Soon-Yau Cheong is the founder of Sooner.ai. It helps businesses to devise and implement AI strategies. Past projects include helping ARM to research using computer vision in automotive camera, and scaling up federated learning software infrastructure with Samsung Research. He has a wide interest in many AI domains including computer vision, NLP and productizing AI. Soon-Yau is also the author of “Hands-on Image Generation with TensorFlow” where he implemented many state-of-the-art models from scratch.
Workshop | Machine Learning | Intermediate-Advanced
In this workshop you will learn when and why federated learning should be used, basic algorithms for implementing it, as well as more advanced ones covering a variety of use-cases. Towards the end of the workshop participants will be offered a hands-on experience of training a federated model together…more details
Mikhail is a Research Staff Member at IBM Research and MIT-IBM Watson AI Lab in Cambridge, Massachusetts. His research interests are Model fusion and federated learning; Algorithmic fairness; Applications of optimal transport in machine learning; Bayesian (nonparametric) modeling and inference. Before joining IBM, he completed Ph.D. in Statistics at the University of Michigan, where he worked with Long Nguyen. He received his bachelor’s degree in applied mathematics and physics from the Moscow Institute of Physics and Technology.
Chaitanya Kumar is a Machine Learning Engineer with the Real World AI team in the IBM Research Singapore Lab, where his work focuses on Federated Learning. Owing to his background in distributed systems, he contributes to the orchestration and deployment of Federated Learning pipelines.
Dr. Laura Wynter is the head of the RealWorld AI team at the IBM Research Singapore Lab. Laura has degrees from MIT and the Ecole des Ponts (Paris, France). Her areas of expertise involve the use of AI as well as optmization, equilibrium modeling and statistics-based methods for enabling effective real-time decision making for planning and operational problems in numerous domains. She has been named an IBM Master Inventor. Her work spans the full lifecycle of a research solution from the definition of the research problem and its characterization, to the development of effective algorithms, to collaborations with the IBM software division culminating in the creation of commercial software products from the models and algorithms developed.
Workshop | Deep Learning | NLP | All Levels
Over the past few years speech synthesis or text-to-speech (TTS) has seen rapid advances thanks to deep learning. As anyone who owns a voice assistant will know, artificial voices are becoming more and more natural and convincing. The good news is you can recreate this impressive technology yourself, using high quality open-source tools.
In this workshop, we’ll learn all about TTS and create a custom speech synthesis system from scratch. We’ll take a look at the development of TTS systems up to the present day, investigate the challenges that researchers are still grappling with, and walk through and end-to-end example of creating a deep learning-based TTS system – including data preparation, training, inference and evaluation. This workshop doesn’t require any prior knowledge of TTS or deep learning…more details
Alex Peattie is the co-founder and CTO of Peg, a technology platform helping multinational brands and agencies to find and work with top YouTubers. Peg is used by over 1500 organisations worldwide including Coca-Cola, L’Oreal and Google.
An experienced digital entrepreneur, Alex spent six years as a developer and consultant for the likes of Grubwithus, Huckberry, UNICEF and Nike, before joining coding bootcamp Makers Academy as senior coach, where he trained hundreds of junior developers. Alex was also a technical judge at this year’s TechCrunch Disrupt conference.
Workshop | NLP | Intermediate-Advanced
Transformers have taken the AI research and product community by storm. We have seen them advancing multiple fields in AI such as NLP, Computer Vision, Robotics. In this talk, I will be giving some background in Conversational AI, NLP and Transformers based Large Scale Language Models such as BERT and GPT-3…more details
Chandra Khatri is the Chief Scientist and Head of AI at Got It AI, wherein, his team is transforming AI space by leveraging state-of-the-art technologies to deliver the world’s first fully autonomous Conversational AI system. Under his leadership, Got It AI is democratizing Conversational AI and related ecosystems through automation. Prior to Got-It, Chandra was leading various AI applied and research groups at Uber, Amazon Alexa and eBay.
At Uber, he was leading Conversational AI, Multi-modal AI, and Recommendation Systems. At Amazon he was the founding member of the Alexa Prize Competition and Alexa AI, wherein he was leading the R&D and got the opportunity to significantly advance the field of Conversational AI, particularly Open-domain Dialog Systems, which is considered as the holy-grail of Conversational AI and is one of the open-ended problems in AI. And at eBay he was driving NLP, Deep Learning, and Recommendation Systems related applied research projects.
He graduated from Georgia Tech with a specialization in Deep Learning in 2015 and holds an undergraduate degree from BITS Pilani, India. His current areas of research include Artificial and General Intelligence, Democratization of AI, Reinforcement Learning, Language and Multi-modal Understanding, and Introducing Common Sense within Artificial Agents.
Tutorial | Machine Learning | Beginner-Intermediate
Faces are a fundamental piece of photography, and building applications around them has never been easier with open-source libraries and pre-trained models. In this tutorial, we’ll help you understand some of the computer vision and machine learning techniques behind these applications. Then, we’ll use this knowledge to develop our own prototypes to tackle tasks such as face detection (e.g. digital cameras), recognition (e.g. Facebook Photos), classification (e.g. identifying emotions), manipulation (e.g. Snapchat filters), and more…more details
Gabriel is the founder of Scalar Research, a full-service artificial intelligence & data science consulting firm. Scalar helps companies tackle complex business challenges with data-driven solutions leveraging cutting-edge machine learning and advanced analytics.
Previously, Gabriel was a B.S. & M.S. student in computer science at Stanford, where he conducted research on computer vision, deep learning, and quantum computing. He’s also spent time at Google, Facebook, startups, and investment firms.
Half-Day Training | Deep Learning | Intermediate-Advanced
In this session, participants will be introduced to recent advances in audio-visual speech enhancement and separation, which has a variety of different applications…more details
Prof. Zheng-Hua Tan is a Professor of Machine Learning and Speech Processing, a Co-Head of the Centre for Acoustic Signal Processing Research (CASPR), and Machine Learning Research Group Leader in the Department of Electronic Systems at Aalborg University, Denmark. Prof. Zheng-Hua Tan was a Visiting Scientist/Professor at the Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology (MIT), Cambridge, USA, an Associate Professor in the Department of Electronic Engineering at Shanghai Jiao Tong University, China, and a postdoctoral fellow at AI Spoken Language Lab, in the Department of Computer Science at KAIST, Korea. He received the B.S. and M.S. degrees in electrical engineering from Hunan University, China, in 1990 and 1996, respectively, and the Ph.D. degree in electronic engineering from Shanghai Jiao Tong University, China, in 1999. His research interests include machine learning, deep learning, pattern recognition, speech and speaker recognition, noise-robust speech processing, multimodal signal processing, and social robotics. He has over 200 publications. He edited the book Automatic Speech Recognition on Mobile Devices and over Communication Networks (Springer, 2008). He is the elected Chair of the IEEE Machine Learning for Signal Processing Technical Committee. He is an Associate Editor for the IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH AND LANGUAGE PROCESSING. He has served as an Editorial Board Member/Associate Editor for several journals including Computer Speech and Language, and Digital Signal Processing. He was a Lead Guest Editor of the IEEE Journal of STSP and a Guest Editor of several journals including Neurocomputing. He was the General Chair of IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP 2018), Aalborg, Denmark, and was a Program Co-Chair for IEEE Workshop on Spoken Language Technology (SLT 2016), San Diego, California, USA. He has served as a Chair, Program Co-chair, Area and Session Chair, and Tutorial Speaker of many international conferences. He is a Senior Member of the IEEE.
Daniel Michelsanti is an Industrial Postdoctoral Researcher at Demant and Aalborg University. He has a PhD in Electrical and Electronic Engineering obtained at Aalborg University. Currently, he is investigating cutting-edge technologies for next-generation hearing assistive devices, with the goal of improving the life quality of people with hearing loss.
Full-Day Training | Machine Learning | Beginner-Intermediate
This session is a hands-on introduction to Machine Learning in Python with scikit-learn. You will learn to build and evaluate predictive models on tabular data using the main tools of the Python data-science stack (Jupyter, numpy, pandas, matplotlib and scikit-learn)…more details
Olivier Grisel is a machine learning engineer at Inria. He is a member of the team of maintainers of the scikit-learn project. Scikit-learn is an Open Source machine learning library written in Python. His work is supported by the Fondation Inria and its partners
Full-Day Training | NLP | Deep Learning | Intermediate-Advanced
Being specialized in domains like computer vision and natural language processing is no longer a luxury but a necessity which is expected of any datascientist in today’s fast-paced world! With a hands-on and interactive approach, we will understand essential concepts in NLP along with extensive hands-on examples to master state-of-the-art tools, techniques and methodologies for actually applying NLP to solve real- world problems. We will leverage machine learning, deep learning and deep transfer learning to learn and solve popular tasks using NLP including NER, Classification, Recommendation \ Information Retrieval, Summarization, Classification, Language Translation, Q&A and Topic Models…more details
Anuj Gupta is a head the Machine Learning and Data Science teams at Vahan. Prior to this, he was heading ML efforts for Intuit, Huawei Technologies, Freshworks, Chennai and Airwoot, Delhi. He did his masters in theoretical computer science from IIIT Hyderabad and he dropped out of his Phd from IIT Delhi to work with startups.
He is a regular speaker at ML conferences like Pydata, Nvidia forums, Fifth Elephant, Anthill. He has also conducted a bunch of workshop attended by machine learning practitioners. He is also the co-organizer for one of the early Deep Learning meetups in Bangalore. He is also Editor of “”Anthill-2018″” – deep learning focused conference by HasGeek.
Half-Day Training | Machine Learning | Beginner
The objective of the session is to provide some basic understanding of Python as a language to be used for data processing. Python syntax is very readable and easy to work with, and its rich ecosystem of libraries makes it one of the most popular programming languages in the World.
We will see some common tools and characteristics of Python that are basic to analyse data, like how to import data from files and to generate results in multiple formats. We will also see some ways to speed-up the processing of data.
This workshop is aimed at people with little to no knowledge of Python, though some programming knowledge is required, even if it’s in a different language…more details
Jaime Buelta has been a professional programmer since 2002 and a full-time Python developer since 2010. He has developed software for a variety of fields, focusing, in the last 10 years, on developing web services in Python in the gaming and finance industries. He is a strong proponent of automating everything to make computers do most of the heavy lifting, so humans can focus on the important stuff. He published his first book, “Python Automation Cookbook”, in 2018 (now updated recently with an extended second edition), followed by “Hands-On Docker for Microservices with Python” the following year. He is currently working as Software Architect in Double Yard in Dublin, Ireland, and is a regular speaker at PyCon Ireland.
Half-Day Training | Quant Finance | Machine Learning | Intermediate
The rapid progress in machine learning (ML) and the massive increase in data availability has enabled novel approaches to quantitative investment and increased the demand for the application of data science to develop discretionary and automated trading strategies.
This workshop covers popular ML use cases for the investment industry. In particular, it focuses on how ML fits into the workflow of developing a trading strategy, from the engineering of financial features to the development of an ML model that generates tradable signals, the backtesting of a trading strategy that acts on these signals and the evaluation of its performance.
We’ll use common Python data science and ML libraries as well as Zipline, Pyfolio, and Alphalens. The code examples will be presented using jupyter notebooks and are based on the second edition of my book ‘Machine Learning for Algorithmic Trading’…more details
Stefan is the founder and Lead Data Scientist at Applied AI. He advises Fortune 500 companies, investment firms and startups across industries on data & AI strategy, building data science teams, and developing machine learning solutions. Before his current venture, he was a partner and managing director at an international investment firm where he built the predictive analytics and investment research practice. He also was a senior executive at a global fintech company with operations in 15 markets.
Earlier, he advised Central Banks in emerging markets, worked for the World Bank, raised $35m from the Gates Foundation to cofound the Alliance for Financial Inclusion, and has worked in six languages across Asia, Africa, and Latin America. Stefan holds Master degrees in Computer Science from Georgia Tech and in Economics from Harvard and Free University Berlin and is a CFA Charterholder. He is the author for ‘Machine Learning for Algorithmic Trading’ and has been teaching data science at Datacamp and General Assembly.
Half-Day Training | Big Data | Beginner
This tutorial will introduce you to the wonderful world of Bayesian data science through the lens of probabilistic programming in Python. In the first half of the tutorial, we will introduce the key concepts of probability distributions via hacker statistics, hands-on simulation, and telling stories of the data-generation processes. We will also cover the basics of joint and conditional probability, Bayes’ rule, and Bayesian inference, all through hands-on coding and real-world examples. In the second half of the tutorial, we will use a series of models to build your familiarity with PyMC3, showcasing how to perform the foundational inference tasks of parameter estimation, group comparison (for example, A/B tests and hypothesis testing), and arbitrary curve regression…more details
Hugo Bowne-Anderson is a data scientist, writer, educator & podcaster. His interests include promoting data & AI literacy/fluency, helping to spread data skills through organizations and society and doing amateur stand up comedy in NYC. He does many of these at DataCamp, a data science training company educating over 3 million learners worldwide through interactive courses on the use of Python, R, SQL, Git, Bash and Spreadsheets in a data science context. He has spearheaded the development of over 25 courses in DataCamp’s Python curriculum, impacting over 170,000 learners worldwide through my own courses. He hosts and produce the data science podcast DataFramed, in which he uses long-format interviews with working data scientists to delve into what actually happens in the space and what impact it can and does have. He earned PhD in Mathematics from the University of New South Wales, Australia and has conducted biomedical research at the Max Planck Institute in Germany and Yale University, New Haven.
Workshop | Big Data | All Levels
In many data science ecosystems data frame is a pivotal object. It is not only very useful conceptually, but also ensures that data transformation operations can be performed efficiently. Therefore packages like data.table in R or pandas in Python are star players.
With the Julia language the situation is different because it gives you the speed out of the box. Therefore the DataFrames.jl package is designed to be a sidekick that conveniently supports your core data analysis pipeline. It has a more focused functionality than e.g. pandas, but at the same time it seamlessly integrates with the whole Julia data science ecosystem.
During this workshop, using hands-on examples, I will discuss the design principles behind DataFrames.jl and walk you through key functionalities provided by this package. All presented materials will be made available before the workshop in a blog post on https://bkamins.github.io/…more details
Bogumił Kamiński is Head of Decision Analysis and Support Unit at Warsaw School of Economics, Poland, and Adjunct Professor at Data Science Laboratory, Ryerson University, Canada. His research interests are techniques of large scale mathematical modelling of complex systems combining simulation, optimization, and machine learning. A particular area of his expertise are agent based simulation and modeling and analysis of complex networks.
Bogumił is one of the core developers of DataFrames.jl package for data wrangling in the Julia language. He is also a top answerer for the [julia] tag on StackOverflow and regularly discusses a wide range of data science related topics on his blog https://bkamins.github.io/.
Tutorial | Machine Learning | Intermediate-Advanced
Advances in information extraction have enabled the automatic construction of large knowledge graphs (KGs) like DBpedia, YAGO, Wikidata of Google Knowledge Graph. Learning rules from KGs is a crucial task for KG completion, cleaning and curation. This tutorial presents state-of-the-art rule induction methods, recent advances, research opportunities as well as open challenges along this avenue...more details
Daria Stepanova is a lead research scientist at Bosch Center for Artificial Intelligence. Her research interests include knowledge representation and reasoning, machine learning and neuro-symbolic AI. Previously Daria was a senior researcher at Max Plank Institute for Informatics (Germany), where she was heading a group on semantic data. Daria got her PhD in Computational Logic from Vienna University of Technology (Austria) in 2015. Before starting her PhD she worked as a visiting researcher at the School of Computing Science at Newcastle University (UK) in an industrially-oriented project.
Tutorials | Machine Learning | Intermediate
Automated machine learning is the science of building machine learning models in a data-driven, efficient, and objective way. It replaces manual trial-and-error with automated, guided processes. In this tutorial, we will guide you through the current state of the art in hyperparameter optimization, pipeline construction, and neural architecture search. We will discuss model-free blackbox optimization methods, Bayesian optimization, as well as evolutionary and other techniques. We will also pay attention to meta-learning, i.e. learning how to build machine learning models based on prior experience. Moreover, we will give practical guidance on how to do meta-learning with OpenML, an online platform for sharing and reusing machine learning experiments, and how to perform automated pipeline construction with GAMA, a novel, research-oriented AutoML tool in Python…more details
Joaquin Vanschoren is Assistant Professor in Machine Learning at the Eindhoven University of Technology. His research focuses on machine learning, meta-learning, and understanding and automating learning. He founded and leads OpenML.org, an open science platform for machine learning. He received several demo and open data awards, has been tutorial speaker at NeurIPS and ECMLPKDD, and invited speaker at ECDA, StatComp, AutoML@ICML, CiML@NIPS, DEEM@SIGMOD, AutoML@PRICAI, MLOSS@NIPS, and many other occasions. He was general chair at LION 2016, program chair of Discovery Science 2018, demo chair at ECMLPKDD 2013, and he co-organizes the AutoML and meta-learning workshop series at NIPS and ICML. He is also co-editor of the book ’Automatic Machine Learning: Methods, Systems, Challenges’.
Pieter Gijsbers is a Ph.D. student at the Eindhoven University of Technology. His areas of interest are automated machine learning and meta-learning. He is the main author of GAMA, a research-focused open-source AutoML tool. While GAMA is easy to use for end-users, it also allows researchers to try different search techniques and visualize the optimization process. He is a co-author of the Open Source AutoML Benchmark. He is also part of the team working on the openml-python package, providing an easy to use Python interface to OpenML.
Workshop | Deep Learning | Intermediate-Advanced
CNNs, specialized neural networks for Computer Vision tasks, are used in sensitive contexts and exposed in the wild. While extremely accurate, they are also sensitive to imperceptible perturbations that can’t be detected by human eyes. For this reason, they have been targeted by hackers which implemented AI-based techniques for their malicious purposes. During this workshop we are going to learn some synthetic attacking techniques and a defence strategy to mitigate the effect of such attacks and make neural networks more robust to them, while at the same time keeping minimal impact on the accuracy of the model and implementation costs. We would also try to understand if Transformers applied to Computer Vision tasks are immune to Adversarial Attacks…more details
Guglielmo is a Biomedical Engineer with an extensive background in Software Engineering and Data Science applied to different contexts, such as Biotech Manufacturing, Healthcare and DevOps, just to mention the latest, and a lifelong learner.
Currently busy unlocking business value through Deep Learning projects, mostly in Computer Vision (not restricted to this field by the way).
He has been recognized as DataOps Champion at the Streamsets DataOps Summit 2019 and awarded as one of the Top 50 Tech Visionaries at the 2019 Dubai Intercon Conference.
He is also an international speaker and author of the following book:
Hands-on Deep Learning with Apache Spark @Packt https://www.packtpub.com/big-data-and-business-intelligence/hands-deep-learning-apache-spark
Workshop | Deep Learning | Intermediate
Learn the basics of building a PyTorch model using a structured, incremental and from first principles approach. Find out why PyTorch is the fastest growing Deep Learning framework and how to make use of its capabilities: autograd, dynamic computation graph, model classes, data loaders and more.
The main goal of this session is to show you how PyTorch works: we will start with a simple and familiar example in Numpy and “torch” it! At the end of it, you should be able to understand PyTorch’s key components and how to assemble them together into a working model.
We will use Google Colab and work our way together into building a complete model in PyTorch. You should be comfortable using Jupyter notebooks, Numpy and, preferably, object oriented programming…more details
Daniel has been teaching machine learning and distributed computing technologies at Data Science Retreat, the longest-running Berlin-based bootcamp, for more than three years, helping more than 150 students advance their careers.
He writes regularly for Towards Data Science. His blog post “Understanding PyTorch with an example: a step-by-step tutorial” reached more than 220,000 views since it was published.
The positive feedback from the readers motivated him to write the book Deep Learning with PyTorch Step-by-Step, which covers a broader range of topics.
Daniel is also the main contributor of two python packages: HandySpark and DeepReplay.
His professional background includes 20 years of experience working for companies in several industries: banking, government, fintech, retail and mobility.
Workshop | Machine Learning | Beginner-Intermediate
In this session, we will work through the basics of solving a classification-based machine learning problem using python and scikit-learn, and do a comparative study of two popular algorithms…more details
Yamini Rao is a Developer Advocate for IBM. She complies various developer scenarios and training material, including demos, writing blog posts, creating audio-visual artefacts and giving hands-on workshops and training sessions based on IBM Cloud technologies. She works with various developer communities across the UK. As a developer advocate, Yamini is involved in public speaking engagements at conferences and meetups and also organises some of these events as part of the role. The other part of Advocacy involves closing the loop, to collect feedback from the developer community about how they are using the technology and channel this back to the engineering and product management teams. She has a background in computer science and has previously worked as an Implementation Engineer for various IBM Analytical tools.
Tutorial | NLP | Deep Learning | Beginner-Intermediate
Have you wondered what is the technology behind the GPT models? In this talk, we are going to discuss the Transformer neural networks, introduced in 2017…more details
A former theoretical physicist turned machine learning engineer, Olga is now building a smart data annotation platform at Scaleway as a technical product manager. On the community side, she enjoys blogging about the latest advancements in AI both in and out of working hours. Some of her writing can be seen on medium.com/@olgapetrova_92798.
Workshop | Quant Finance | Intermediate-Advanced
This target of this workshop is twofold. On one hand, it is familiarizing attendees with mechanics of reinforcement learning (RL) applied to financial environments. On the other side, it aims to uncover key differences between popular RL applications (as playing video games) and financial ones, ignoring which inevitably will lead to losses of time and capital. With such insights and code boilerplates, attendees will be able to avoid harsh mistakes and implement environment-driven strategies faster…more details
Alex Honchar is a tech entrepreneur and educator. Currently, he is co-founder and ML director at Neurons Lab – a consulting firm specializing in healthcare, finance, and IoT. Also, he writes a popular blog on Medium about machine learning applications and leadership. Previously he worked as an independent consultant with SMBs and startups on rapid go-to-market ML solutions and taught machine learning courses at the University of Verona and Ukrainian Catholic University.
Workshop | Responsible AI | Beginner
This workshop offers a gentle introduction to reproducible and elegantly formatted document generation with R Markdown. R Markdown presents a framework for reproducible workflows. It allows you to use multiple languages including R, Python, and SQL and helps you automate the production of HTML or PDF reports by relying on the power of Pandoc together with Lua-filters.
Participants will learn how to implement literate programming practices to make their workflows fully reproducible and produce automated reports. We will first cover the basics of narrative text and code integration. Then, we focus on working on a template that is fully optimized for two different output formats, HTML and PDF. While in the stage of explorative data analysis and with an eye on content only, our template allows you to produce beautiful HTML reports of your analyses, optimized for the interactive exploration of your data. While in the stage of dissemination and with an eye on the presentation of results, our template allows you to produce beautifully typeset PDF reports, ready to be circulated or published any time…more details
Julia Schulte-Cloos is a Marie Skłodowska-Curie funded LMU Research Fellow at the Geschwister Scholl Institute of Political Science at LMU Munich. Her main research interests lie in comparative politics, political behavior, research methodology, and reproducibility. Julia Schulte-Cloos has earned her PhD from the European University Institute.
Workshop | Machine Learning | All Levels
By completing this workshop, you will develop an understanding of the different freely available RS data sources out there and open-source software tools that can be used for analysing these...more details
I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year’s experience in conducting academic research (published in high level peer-reviewed international