Former teaching material

This is a part of the teaching material I created when I was associate professor (maître de conférences, HDR) at INSA de Lyon and the LIRIS laboratory from 2005 to the end of 2021.

Cours AI and Data Analytics (UCBL, Master 3EA, Parcours ASI, 2026)

Cours+TD+TP Deep Learning and Differentiable Programming (IF - 5ème année 2019-2021)

Part 1 - Introduction 1.1: Introduction: machine learning, a couple of applications [36 slides] 1.2: A short history of deep learning [8 slides] 1.3: An extremely short crash course on fitting and generalization [16 slides] Part 2 - Neural Networks and PyTorch 2.1: Frameworks and Tensors {+PyTorch} [24 slides] 2.2: (Generalized) Linear Models {+PyTorch} [32 slides] 2.3: Multi layer models + Universal approximation theorem {+PyTorch} [32 slides] 2.4: Train/Validation/Test split; Tensorboard {+PyTorch} [14 slides] 2.5: Gradient Backpropagation & Autograd {+PyTorch} [17 slides] 2.6: Stochastic Gradient Descent (SGD) and Variants (ADAM, RMSProp) {+PyTorch} [15 slides] 2.7: Shift invariance (equivariance) and Convolutions {+PyTorch} [38 slides] (see also blog post on shift equivariance) Part 3 - Scaling up: computer vision, transfer learning, visualization 3.1: Computer Vision [41 slides] 3.2: Visualization of learned knowledge [20 slides] 3.3: Transfer learning [19 slides] 3.4: Semi-supervised and self-supervised learning [8 slides] 3.5: GPUs - software {+CUDA, +PyTorch} [14 slides] Part 4 - Structure: sequences, graphs, attention 4.1: Recurrent Neural Networks and variants [35 slides] 4.2: Attention mechanisms in computer vision [41 slides] 4.3: Graphs and relational reasoning [37 slides] 4.4: Self-attention and transformers [19 slides] Part 5 - Advanced applications 5.1: Reinforcement Learning [25 slides] 5.2: Should we model or learn? [27 slides] 5.3: Learning robot navigation [61 slides] 5.4: Machine Learning as an Experimental Science [9 slides] 5.5: Conclusions [4 slides] Part 6 - A gentle introduction into learning theory 5.1: A gentle introduction into learning theory [45 slides] 5.2: A concrete application of the usage of learning theory in DL [35 slides] PyTorch sources: The full PyTorch sources of the slides are here. Pratical Excercise session: (will be held by Edward Beeching, web site will be published shortly.) Neural models in python for object detection and future forecasting. [Project page]

TD Advanced algorithms and data structures for Artificial Intelligence (IF - 3ème année)

With Christine Solnon: Shortest path problems, topological orderings of graphs, minimum spanning trees, the travelling saleman problem etc.

Cours+TD+TP Computer Architecture / Architectures d'ordinateurs (IF - 3ème année)

Session 1: Instruction Set Architecture, assembler (1) [PDF] Session 2: Instruction Set Architecture, assembler (2) [PDF] Session 3: Building a RISC processor from scratch. Instruction level parallelism [PDF] Session 4: Super-scalar processors, multi-threading, multi-core; ARM vs. x86 [PDF] Session 5: Memory hierarchy : cache, virtual memory [PDF] Session 6: Massively parallel processors (GPUs) [PDF] Full content on Moodle (in French)

Cours Robotique et vision par ordinateur OT (Option transversale 5A) "Robotique")

Vision for mobile robotics Visual SLAM with Kinect Object and face detection : deep learning Human-robot interaction : pose estimation Human-robot interaction : gesture recognition Human-robot interaction : face-to-face interaction The slides [PDF]

Cours+TD+TP Lanières SCAN et L - Groupes 72,54 (PC - 2ème année)

Full content on Moodle (in French): Winter Semester (S3), Summer Semester (S4), Database part (S3) Algorithmics and the java programming language: Classes and object oriented programming User interface design: event based programming Dynamic data structures: lists, trees Mini projet (Screenshots of previous miniprojects) Useful links: A cute java applet which visualizes how various data structures work.