ODSC Europe Virtual Conference 2021

Preliminary schedule announced!

Europe Trainings
-Tuesday, 8th June
-Wednesday, 9th June
Thursday, 10th June
Europe Workshops & Tutorials
-Tuesday, 8th June
-Wednesday, 9th June
Thursday, 10th June
Europe Talks
-Wednesday, 9th June
Thursday, 10th June
-Tuesday, 8th June
-Wednesday, 9th June
Thursday, 10th June
-Tuesday, 8th June
-Wednesday, 9th June
Thursday, 10th June
-Wednesday, 9th June
Thursday, 10th June
10:00 - 13:00
Session by Jaime Buelta Coming Soon!

Half-Day Training

Session by Jaime Buelta Coming Soon! image
Jaime Buelta
Software Architect | Double Yard
10:00 - 13:00
Session by Radovan Kavicky Coming Soon!

Half-Day Training

Session by Radovan Kavicky Coming Soon! image
Radovan Kavicky
President, Data Science Instructor, Founder | GapData Institute, PyData Bratislava
10:00 - 13:00
Hands-on Machine Learning Engineer with scikit-learn

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

Hands-on Machine Learning Engineer with scikit-learn image
Olivier Grisel
Software Engineer, Core Developer | Inria, Scikit-learn
10:00 - 16:30
Advanced NLP: From Essentials to Deep Transfer Learning

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

Advanced NLP: From Essentials to Deep Transfer Learning image
Dipanjan (DJ) Sarkar
Data Science Lead, Applied Materials | Google Developer Expert - ML
Advanced NLP: From Essentials to Deep Transfer Learning image
Anuj Gupta, PhD
Head of Machine Learning & Data Science | Vahan Inc.
13:30 - 16:30
How to Build and Test a Trading Strategy Using ML

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

How to Build and Test a Trading Strategy Using ML image
Stefan Jansen
Founder & Lead Data Scientist | Applied Artificial Intelligence
13:30 - 16:30
Bayesian Data Science: Probabilistic Programming

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

Bayesian Data Science: Probabilistic Programming image
Hugo Bowne-Anderson, PhD
Head of Data Science Evangelism and Marketing | Coiled
10:15 - 11:45
Session by Guglielmo Iozzia Coming Soon!

Workshop

Session by Guglielmo Iozzia Coming Soon! image
Guglielmo Iozzia
Associate Director – Business Tech Analysis, IT & AI | MSD
10:15 - 11:45
Session by Alessio Lomuscio, PhD Coming Soon!

Workshop

Session by Alessio Lomuscio, PhD Coming Soon! image
Alessio Lomuscio, PhD
Royal Academy of Engineering Chair in Emerging Technologies | Imperial College London
10:15 - 11:45
PyTorch 101: Building a Model Step-by-Step

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

PyTorch 101: Building a Model Step-by-Step image
Daniel Voigt Godoy
Manager, Financial Advisory Analytics, Dean | Deloitte, Data Science Retreat
10:15 - 11:45
Tutorial on Automated Machine Learning

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

Tutorial on Automated Machine Learning image
Joaquin Vanschoren, PhD
Assistant Professor of Machine Learning | Eindhoven University of Technology
Tutorial on Automated Machine Learning image
Pieter Gijsbers
PhD student | Eindhoven University of Technology
10:15 - 11:45
Mastering Gradient Boosting with CatBoost

Workshops | Machine Learning | Intermediate

 

This workshop will feature a comprehensive tutorial on using CatBoost library.
We will walk you through all the steps of building a good predictive model.
We will cover such topics as:
– Working with different types of features, numerical and categorical
– Working with inbalanced datasets
– Using cross-validation
– Understanding feature importances and explaining model predictions
– Tuning parameters of the model
– Speeding up the training..more details

Mastering Gradient Boosting with CatBoost image
Anna Veronika Dorogush
ML Lead | Yandex
11:55 - 13:25
Rule Induction and Reasoning in Knowledge Graphs

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

Rule Induction and Reasoning in Knowledge Graphs image
Daria Stepanova, PhD
Research Scientist | Bosch Center for AI
11:55 - 13:25
Introduction To Face Processing With Computer Vision

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

Introduction To Face Processing With Computer Vision image
Gabriel Bianconi
Founder | Scalar Research
13:40 - 15:10
Explainable Artificial Intelligence Explained

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 networksmore details

Explainable Artificial Intelligence Explained image
Karol Przystalski
CTO | Codete
13:40 - 15:10
Session by Daniel Zakrisson Coming Soon!

Workshop

Session by Daniel Zakrisson Coming Soon! image
Daniel Zakrisson
CEO and Co-founder | Scaleout
13:40 - 15:10
Automatic and Explainable Machine Learning with H2O

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

Automatic and Explainable Machine Learning with H2O image
Jo-fai Chow, PhD
Senior Data Science Evangelist | H2O.ai
13:40 - 15:10
Responsible Data Science Using Bias-Dashboards

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

Responsible Data Science Using Bias-Dashboards image
Ramon van den Akker,PhD
Principal Data scientist | Associate Professor, Econometrics | de Volksbank | Tilburg University
Responsible Data Science Using Bias-Dashboards image
Daan Knoope
Ai Engineer | de Volksbank
Responsible Data Science Using Bias-Dashboards image
Joris Krijger
Ai & Ethics Specialist | de Volksbank
15:20 - 16:50
Session by Mikhail Yurochkin, PhD Coming Soon!

Workshop

Session by Mikhail Yurochkin, PhD Coming Soon! image
Mikhail Yurochkin, PhD
Research Staff Member | IBM Research and MIT-IBM Watson AI Lab
15:20 - 16:50
Teaching Machines to Talk: Modern Speech Synthesis with Deep Learning

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

Teaching Machines to Talk: Modern Speech Synthesis with Deep Learning image
Alex Peattie
Co-founder, CTO | Peg
Deep Learning for Anomaly Detection

Talk | Deep Learning | All Levels

 

This talk reviews a set of relevant deep learning model architectures including autoencoders, variational auto-encoders, generative adversarial networks and sequence-to-sequence methods, and addresses how they can be applied to the task of anomaly detection, comparing them in terms of training, inference and storage costs. Anomaly detection using each of these models is explored as a function of how they can be applied to first model normal behavior, and then this knowledge is exploited to identify deviations. In addition, we provide practical guidance for the successful implementation of anomaly detection systems within enterprises across key metrics like interpretability, reduction of false positives and scalability…more details

Deep Learning for Anomaly Detection image
Nisha Muktewar
Research Engineer | Cloudera Fast Forward Labs
10:00 - 10:45
Session Title by Sandra Wachter, PhD Coming Soon!

Talk

Session Title by Sandra Wachter, PhD Coming Soon! image
Sandra Wachter, PhD
Associate Professor and Senior Research Fellow, Law and Ethics of AI | Oxford Internet Institute, University of Oxford
Can Your Model Survive the Crisis: Monitoring, Diagnosis and Mitigation

Talk | Deep Learning | Machine Learning | Intermediate-Advanced

 

As the world rapidly changing around us, business across industries are treading carefully with unprecedented challenges and, if lucky enough, new opportunities. Due to their statistical assumption of generalizable patterns from the past, machine learning models are facing more scepticism about their validity in the world we now live in. It is more crucial than ever for data scientists to keep close eye on our beloved models in production, understand the impact of business changes on them, and steer promptly from potential pitfalls. In this session, I will share some experience of model monitoring and diagnosis from a leading UK fintech company…more details

Can Your Model Survive the Crisis: Monitoring, Diagnosis and Mitigation image
Jiahang Zhong, PhD
Head of Data Science | Zopa Ltd
Data Science for Vaccines Research and Development

Business Talk

Data Science for Vaccines Research and Development image
Dr. Duccio Medini
Senior Director, Head Data Science and Clinical Systems | Glaxo Smith Kline
10:50 - 11:35
Session Title by Frank Hutter, PhD Coming Soon!

Talk | All Levels

Session Title by Frank Hutter, PhD Coming Soon! image
Frank Hutter, PhD
Professor Of Computer Science | University of Freiburg
10:50 - 11:35
Session Title by Dr. Fabian Theis Coming Soon!

Talk