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Some Current Europe Instructors

Click on the toggle bars for information around sessions and instructors. Check full Speaker and instructor line-up here.


Europe Trainings – BST (UTC +1)
-Tuesday, 8th June
-Wednesday, 9th June
Thursday, 10th June
Europe Workshops – BST (UTC +1)
-Tuesday, 8th June
-Wednesday, 9th June
Thursday, 10th June
-Tuesday, 8th June
-Wednesday, 9th June
Thursday, 10th June
-Tuesday, 8th June
-Wednesday, 9th June
Thursday, 10th June
10:00 - 13:00
Audio-Visual Speech Enhancement and Separation Based on Deep Learning

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

Audio-Visual Speech Enhancement and Separation Based on Deep Learning image
Prof. Zheng-Hua Tan
Professor of Machine Learning and Speech Processing | Aalborg University
Audio-Visual Speech Enhancement and Separation Based on Deep Learning image
Dr. Daniel Michelsanti
Industrial Postdoc | Demant Enterprise A/S and Aalborg University
10:00 - 16:30
NLP Fundamentals

Full-Day Training | NLP | Machine Learning | All Levels

 

In this course, we will go through Natural Language Processing fundamentals, such as pre-processing techniques,tf-idf, embeddings, and more. It will be followed by practical coding examples, in python, to teach how to apply the theory to real use cases...more details

NLP Fundamentals image
Leonardo De Marchi
Head of Data Science and Analytics | Badoo (now MagicLab, which owns several apps)
10:00 - 16:30
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
Anuj Gupta, PhD
Head of Machine Learning, Author of "Practical Natural Language Processing" | Vahan Inc.
13:30 - 16:30
Introduction to Data Analysis Using Pandas

Half-Day Training | Data Analysis | Beginner-Intermediate

 

Working with data can be challenging: it often doesn’t come in the best format for analysis, and understanding it well enough to extract insights requires both time and the skills to filter, aggregate, reshape, and visualize it. This session will equip you with the knowledge you need to effectively use pandas – a powerful library for data analysis in Python – to make this process easier.

Pandas makes it possible to work with tabular data and perform all parts of the analysis from collection and manipulation through aggregation and visualization. While most of this session focuses on pandas, during our discussion of visualization, we will also introduce at a high level matplotlib (the library that pandas uses for its visualization features, which when used directly makes it possible to create custom layouts, add annotations, etc.) and seaborn (another plotting library, which features additional plot types and the ability to visualize long-format data)....more details

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Stefanie Molin
Data Scientist, Software Engineer, Author of Hands-On Data Analysis with Pandas | Bloomberg
13:30 - 16:30
Basic Python for Data Processing

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

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Jaime Buelta
Software Architect | Double Yard
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
Dataframes.jl: a Perfect Sidekick for Your Next Data Science Project

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

Dataframes.jl: a Perfect Sidekick for Your Next Data Science Project image
Bogumił Kamiński
Head of Decision Analysis and Support Unit | Adjunct Professor, Data Science Laboratory | Warsaw School of Economics | Ryerson University
10:15 - 11:45
Classification Algorithms using Python and Scikit-Learn

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

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Yamini Rao
Developer Advocate | IBM
10:15 - 11:45
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

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Daria Stepanova, PhD
Research Scientist | Bosch Center for AI
10:15 - 11:45
Mastering Gradient Boosting with CatBoost

Workshop | 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
Stanislav Kirillov
Senior Software Developer | Yandex
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
Adversarial Attacks and Defence in Computer Vision 101

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 Attacksmore details

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Guglielmo Iozzia
Associate Director – Business Tech Analysis, IT & AI | MSD
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

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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
11:55 - 13:25
Introduction to Transformers for NLP: Where We Are and How We Got Here

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

Introduction to Transformers for NLP: Where We Are and How We Got Here image
Olga Petrova, PhD
AI Product Manager | Scaleway
11:55 - 13:25
Hands-on RL in Finance: Playing Atari vs Playing Markets

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 fastermore details

Hands-on RL in Finance: Playing Atari vs Playing Markets image
Alex Honchar
Director | Neuron Labs
11:55 - 13:25
Reproducible and Automated Report Generation

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 timemore details

Reproducible and Automated Report Generation image
Julia Schulte-Cloos, PhD
Marie Skłodowska-Curie Research Fellow | University of Munich
11:55 - 13:25
Machine Learning For Remote Sensing Based Landcover Change Detection

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

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Minerva Singh, PhD
Deep Learning and Machine Learning Instructor
11:55 - 13:25
Machine Learning for Economics and Finance in TensorFlow 2

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

Machine Learning for Economics and Finance in TensorFlow 2 image
Isaiah Hull
Senior Economist | Research Division of Sveriges Riksbank