| Course label : | Theoretical foundations of machine learning 1 - Bayesian Learning |
|---|---|
| Teaching departement : | EEA / Electrotechnics - Electronics - Control Systems |
| Teaching manager : | Mister PIERRE-ANTOINE THOUVENIN / Mister PIERRE CHAINAIS |
| Education language : | |
| Potential ects : | 0 |
| Results grid : | |
| Code and label (hp) : | MR_DS_S3_TF1 - Theoretical foundations of mac |
Education team
Teachers : Mister PIERRE-ANTOINE THOUVENIN / Mister PIERRE CHAINAIS
External contributors (business, research, secondary education): various temporary teachers
Summary
· Probabilistic modeling and parameter inference as the main learning task · Bayesian decision theory · Refresher on MCMC · Variational inference · Gaussian processes, and their application to regression · Bayesian optimization, with application to hyperparameter tuning · Bayesian neural networks
Educational goals
After successfully taking this course, students should be able to: · Understand the philosophy of the Bayesian framework, and how it can be used to make decisions under uncertainty · Design probabilistic models for the task at-hand · Run some computational tools for Bayesian inference, either MCMC or variational inference · Know about the limitations of those approaches, and be knowledgeable about topical research questions
Sustainable development goals
Knowledge control procedures
Continuous Assessment
Comments: Labs (60% of the grade) : (min) 0 - 20 (max)
Final exam (40% of the grade) : (min) 0 - 20 (max)
Passing grade 10/20
Online resources
Probabilistic Machine Learning: Advanced Topics, Kevin Murphy (2023) Bayesian Reasoning and Machine Learning, David Barber (2012) Pattern Recognition and Machine Learning, Christopher Bishop (2006) The Bayesian Choice, Christian Robert (2007)
Pedagogy
24 hours, which include 4 labs and a final exam
Sequencing / learning methods
| Number of hours - Lectures : | 12 |
|---|---|
| Number of hours - Tutorial : | 12 |
| Number of hours - Practical work : | 0 |
| Number of hours - Seminar : | 0 |
| Number of hours - Half-group seminar : | 0 |
| Number of student hours in TEA (Autonomous learning) : | 0 |
| Number of student hours in TNE (Non-supervised activities) : | 0 |
| Number of hours in CB (Fixed exams) : | 0 |
| Number of student hours in PER (Personal work) : | 0 |
| Number of hours - Projects : | 0 |
Prerequisites
From the M1 year : Statistics 2, Models for Machine Learning