2022
Master: Advanced topics in Applied Machine Learning
2nd year of Master in Computer Science. Two lectures, design and correction of the project, planning.
- Recalls of machine learning, machine learning frameworks, first part project
- Overview of adversarial machine learning, model calibration
Project: Creation and evaluation of fill-in-the-blank notebooks (part 1 on preprocessing and part 3 on adversarial examples)
Master: Introduction to Machine Learning
2nd year of Master in Space Science. Six sessions, including four based on the Machine Learning Refined book and one based on the Applied Machine Learning course of Andreas C. Müller. Summary slides.
- Recalls of linear algebra: “Machine Learning Refined” book, “Essence of linear algebra” videos serie of 3blue1brown, some formal definitions from the “Mathematics for Machine Learning” book.
- Zero-order optimization: Chapter 2, Appendix B.
- First-order optimization: Chapter 3, “Gradient descent, how neural networks learn” video from 3Blue1Brown.
- Linear regression and linear classification: Chapters 5 and 6
- ML project lifecycle: Data preparation, feature engineering, overfitting & underfitting, model evaluation. Slides.
- Neural Networks (slides), Keras & Convolutional Neural Nets (slides) and Advanced Neural Networks (slides).
- Written examination
2021
Master: Introduction to Machine Learning
2nd year of Master in Space Science. Two introductory lectures on Machine Learning. Slides.
2020
Bachelor: Software engineering 2
3rd year of Bachelor in Computer Science. Four introductory lectures on Machine Learning Engineering. Course given online during lockdown. Quizzes on Moodle. Videos, Slides
- Introduction to Machine Learning: Useful Definitions, Types of Tasks in Machine Learning
- Introduction to Machine Learning: Recalls of Statistics, Model’s elements, Elements of Statistical Learning Theory
- Machine Learning Project Lifecycle: When to (not) use Machine Learning, Goal Definition, Data Collection & Preparation
- Machine Learning Project Lifecycle: Feature Engineering, Choosing and Training a model, Model Evaluation, Feedback loop
