Machine learning and deep learning are cornerstones of the data science industry. As such, not only is it important to have a firm foundation in these topics, it is essential to keep up-to-date on the latest advancements. At ODSC Europe 2021 this June 8th-10th, there will be several training sessions, workshops, and talks on machine learning and deep learning from some of the best and brightest minds in data science and AI. Here are just a few ODSC Europe machine learning and deep learning sessions we are excited about.
Federated Learning: Practice and Modern Algorithms: Mikhail Yurochkin, PhD | Research Staff Member | IBM Research and MIT-IBM Watson AI Lab
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.
In this talk, Frank will discuss the technical methods behind recent progress towards robust and efficient AutoML systems.
Automated Machine Learning with Python – from scikit-learn to auto-sklearn: Katharina Eggensperger and Matthias Feurer | PhD Researchers | University of Freiburg
In this workshop, they will cover how to move from manually constructing and tuning machine learning pipelines to using efficient hyperparameter optimization algorithms and full AutoML using the popular Auto-sklearn library.
Rule Induction and Reasoning in Knowledge Graphs: Daria Stepanova, PhD | Research Scientist | Bosch Center for AI
This tutorial presents state-of-the-art rule induction methods, recent advances, research opportunities as well as open challenges along this avenue.
Finding that Needle! Modern Approaches to Fraud and Anomaly Detection: Aric LaBarr, PhD | Associate Professor of Analytics | Institute for Advanced Analytics at NC State University | Read his preview blog here.
This talk will highlight some modern approaches to anomaly detection – local outlier factors, isolation forests, and classifier adjusted density estimation (CADE).
Relationships Matter: Using Connected Data for Better Machine Learning: Dr. Alicia Frame | Lead Data Scientist | Neo4j
In this session, you’ll learn more about using graph-native ML to make break-through predictions, taking different approaches to graph feature engineering from queries and algorithms to embeddings, and more.
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.
Introduction To Face Processing With Computer Vision: Gabriel Bianconi | Founder | Scalar Research
In this tutorial, we’ll help you understand some of the computer vision and machine learning techniques behind face processing apps like Snapchat and Facebook’s facial recognition software work.
Mastering Gradient Boosting with CatBoost: Stanislav Kirillov | Head of ML Systems Group | Yandex
This workshop will feature a comprehensive tutorial on using the CatBoost library, walking attendees through topics like working with different types of features, numerical and categorical; working with imbalanced datasets; using cross-validation; and more.
Overcoming the Cold Start Problem: How to Make New Tasks Tractable: Azin Asgarian (Applied Research Scientist | Georgian) and Franziska Kirschner, PhD (Research and Product Lead | Tractable) | Read their preview blog here.
In this talk, they will demonstrate how the cold start problem can be addressed by aggregating data across sources and leveraging previously trained models with using domain adaptation and ensemble learning techniques.
Register now and see all ODSC Europe Machine Learning Talks and Sessions
These are just a few of the training sessions, workshops, and talks on machine learning and deep learning that will be featured at ODSC Europe next month. Register now and gain access to all of the best ODSC Europe machine learning talks live and on-demand following the event.