Teaching Activities (Enseignements)

Currently, I teach following courses:

Sûreté de fonctionnement et Retour d'expériences ( Reliability and Feedback Data Collection) ,
4th Year of Engineering in Management opérationnel Maintenance et Maîtrise des risques (M3)

Introduction to Deep Learning and Artificial Intelligence (5th year, Ingénierie de l’Information et des Systèmes (FISE & FISA) :

  • Lectures:
    • Basics  ANN (CM1)
    • CNNs (CM2)
    • RNNs and LSTM (CM3)
  • Practicals (Jupyter notebooks, FISE ONLY):
    •   ANN Basics & classification with MNIST data (TD1, TD2, TD3)
    •  CNN based: (TD4) CIFAR data classification
    • LSTM based:  prediction of time series (TD5)
  • Student Feedback ( in French) 2019-20, 2020-2021 , FISA 2023-24

Introduction to Reinforcement Learning for Optimal Control

Lecture Slides:

Session 1:
Introduction to Reinforcement Learning,
Markov Decision Process,
Backward recursive Relation

Session 2
Dynamic Programming,
Bellman Equation and Bellman Optimality Equation
Discrete time Linear system control

Session 3
Policy Iteration
Policy Iteration Algorithm
Discrete time Linear Quadratic Regulator

Session 4
Temporal Difference
Function Approximation
Reinforcement Learning for DT Nonlinear system

Session 5
Q-functions, Policy Iteration using Q functions,
Q-learning

Session 6
Policy gradient approaches,
Deep deterministic Policy gradient (DDPG)

Tutorials (TD):
TD1 : Policy Iteration based learning Feedback optimal control law for Linear system in Discrete Time
TD2 : Policy Iteration for unknown Linear Systems using neural networks

TD3 :  Hands on Deep Q-N and DDPG using MATLAB

Deep Reinforcement Learning (DQN and DDPG).

  • Environments solved in Tutorials (OpenAI gym based): Cartpole, Inverted Pendulum and Bipedal Walker

 

Artificial Intelligence for Prognostics (5th Year, Department M3)

Neural Networks for decision making
CNNs basics and CNNs for Prognostics
RNNs, LSTMs Basics and Deep LSTMs for Prognostics
Course Slides,
Tutorials: TD1, TD2, TD3.

Control of Mobile Robots (5A IA2R Parcours SIA)
Lab 1: The QCAR (Autonomous Car),

Lab 2: The QBOT (differential drive robot by Quanser)

Lab3: The 3Pi+ Robot

Lab4: The Zumo robot