Short Trainings & Workshops

In this seminar the participants will learn what time series data is and its real-world applications. We will review time series key concepts, decomposition and the most popular forecasting methods. We will also explore clustering methods specifically designed for time series and we will conclude with some real world applications of time series analysis.

Explainability of AI models has become an important tool to deploy effectively many AI solutions, and to increase trust from the stakeholders of these solutions. In this workshop, we will introduce the most popular methods for explaining AI predictions, with a few examples of real use cases. Moreover, we will have a hands-on session with an exercise to try these techniques on a sandbox dataset.

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.

This presentation provides a comprehensive introduction to Bayesian Optimization, a powerful technique for efficiently finding optimal solutions in expensive-to-evaluate functions by building probabilistic models and intelligently selecting where to sample next. We'll discuss how to use BO in robust optimization where an optimum that is resistant to perturbations in the input parameters is desired. Additionally, we'll discuss the use of BO in formulation optimization where both components and their proportions are jointly optimized.

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