Prè-requis
- An undergraduate course in probability.
- It is recommended to have followed either the course of P. Latouche and N. Chopin on « Probabilistic graphical models » or the course of S. Allassonière on « Computational statistics » during the first semester.
- Practical will be in Python and R. A basic knowledge of both languages is required. Nothing fancy, students should simply be able to read and write simple programs and load libraries: going through a basic online tutorial in both languages should be enough.
Objectif du cours
By the end of the course, the students should
- have a high-level view of the main approaches to making decisions under uncertainty.
- be able to detect when being Bayesian helps and why.
- be able to design and run a Bayesian ML pipeline for standard supervised or unsupervised
learning. - have a global view of the current limitations of Bayesian approaches and the research
landscape. - be able to understand the abstract of most Bayesian ML papers.
Organisation des séances
* 8×3 hours of lectures and practice TPs
* 4 hours of « student seminars » for the evaluation.
* All classes and material will be in English. Students may write their final report either in French or English.
The course is open to external auditors, but without any grade, assessment or certificate.
Mode de validation
- Students form groups. Each group reads and reports on a research paper from a list. We strongly encourage a dash of creativity: students should identify a weak point, shortcoming or limitation of the paper, and try to push in that direction. This can mean extending a proof, implementing another feature, investigating different experiments, etc.
- Deliverables are a small report and a short oral presentation in front of the class, in the form of a student seminar, which will take place during the last lecture.
Références
Parmigiani, G. and Inoue, L. 2009:Decision theory: principles and approaches. Wiley.
Robert, C. 2007. The Bayesian choice. Springer.
Murphy, K. 2012. Machine learning: a probabilistic perspective. MIT Press.
Ghosal, S., & Van der Vaart, A. W. 2017. Fundamentals of nonparametric Bayesian inference. Cambridge University Press.
Thèmes abordés
- Decision theory
- 50 shades of Bayes: Subjective and objective interpretations
- Bayesian supervised and unsupervised learning
- Bayesian computation for ML: Advanced Monte Carlo and variational methods
- Bayesian nonparametrics
- Bayesian methods for deep learning
Les intervenants
Rémi BARDENET
(CNRS, Univ. Lille)
Julyan ARBEL
(Inria, Univ. Grenoble-Alpes)
Gabriel VICTORINO CARDOSO
(Mines Paris)
