Short Trainings & Workshops

This workshop will introduce participants to survival analysis, a technique originally used in medicine and now employed for analyzing the expected duration of time until any kind of event happens. During the workshop, participants will be introduced to the basic concepts behind survival analysis. They will then learn how to use them hands-on, by applying different algorithms on data relevant to practical use case, then evaluating them and extracting meaningful insights using Python.

This workshop gives a technical introduction to the main techniques used in reinforcement learning and its applications.The topics of this course are the following:-Introduction to Reinforcement Learning (Markov Decision Processes, Bellman Equation, Dynamic Programming, Monte Carlo methods, SARSA and Q-learning) - 2h-Deep Q Network (DQN) - 1h-Policy Gradient (REINFORCE algorithm) - 1h-Actor-Critic Methods (A2C, A3C and SAC) - 2h-Deterministic Policy Gradient algorithms (DDPG, TD3 and D4PG) - 2h-Trust Region Policy Optimization algorithms (TRPO and PPO) - 1h-Advanced Techniques in Reinforcement Learning (HER and RAINBOW) - 1h-AlphaGo, AlphaGo Zero, AlphaZero and MuZero - 3h-AlphaStar - 1hThe total length of this course is 15h but it can be given in a shortened version with a selection of topics.

This course offers a comprehensive introduction to the field of data science from a technical standpoint. Participants will gain a high-level understanding of how AI algorithms can learn from labeled data, focusing on supervised learning. The course will delve into the broader landscape of AI techniques, with special emphasis on the latest advancements in Large Language Models. The final segment of the presentation is dedicated to exploring real-world applications of AI.

This workshop will introduce participants to Reinforcement Learning, in its simplest versions, and examples of a few business applications.

Under development

In this seminar we will introduce the basic concepts and building blocks behind a Knowledge Graph (nodes, edges, embedding) and how they are used to describe entities and their relationships. We will dive into some use cases where Knowledge Graphs are used in the health domain and we will give an overview of cases where they can be leveraged in drug discovery and other application in the pharmaceutical sector. In the last part, we will briefly showcase how neural networks have been adapted to leverage structures and properties of graphs

This workshop provides an in-depth exploration of Large Language Models (LLMs), Prompt Engineering, and Retrieval Augmented Generation (RAG) systems. It introduces the fundamentals of LLMs, focusing on their capabilities and limitations, followed by practical techniques for crafting effective prompts. The course emphasizes the role of RAG in enhancing the accuracy and relevance of LLM-generated content by retrieving external data. Agentic LLM applications will also be introduced.Through use cases from the Swiss Data Science Center (SDSC), participants learn about the architecture, tools, and performance metrics of RAG systems, with interactive discussions encouraged throughout the workshop. A short hands-on session in Renku, will also provide a practical experience with a toy RAG (https://renkulab.io/projects/lamagioviale/rag-system-handson).

This workshop provides an introduction to recommender systems, focusing on the classical and most commonly employed methods (Content Based Filtering and Collaborative Filtering). The workshop includes presentations of business use cases where such systems have been implemented and succesfully deployed.