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Bayesian machine learning
Lecturers: Rémi Bardenet (CNRS, Univ. Lille) & Julyan Arbel (Inria, Univ. Grenoble-Alpes)
Objective of the course:
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.
* 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.
References
* 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.
Topics:
* 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
* 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
Prerequisites:
* 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.
* 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.
Organization of courses:
* 8x3 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.
* 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.
Validation:
* 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.