UFR Sc Techniques

This course presents the main framework of reinforcement learning (RL). At the end of the course, the student will be able to identify and frame problems under this formalism. He will master the vocabulary associated with the RL field, and will be aware of the main obstacles to tackle when using and developing RL approaches (e.g. exploitation-exploration trade-off, sample efficiency). Moreover students will be capable of understanding and implementing “vanilla” algorithms of the core methods in RL (e.g. Q-learning and policy optimisation) and analyze/apply some specific advanced algorithms (e.g. A2C and PPO).

Module servant à recueillir tous les commentaires des étudiants du MIR, depuis les évaluations des matières jusqu'aux choix des options d'études.

After this course the students will be able to identify the deep learning (DL) approaches to be applied to multiple types of machine learning problems, depending on the task and the data inputs/outputs. Students will know how to build and train advanced DL models with the use of existing publicly available software tools. We will also introduce the main shortcomings and limitations of deep learning techniques such as the problem of interpretation and the exploitation of adversarial attacks.

Objectives: The aim of this course is to give the students the basis of propagation of acoustic waves in sea water.