MATH-435 / 5 credits

Teacher:

Language: English

Remark: pas donné en 2021-22


Summary

This course aims at giving a broad overview of Bayesian inference, highlighting how the basic Bayesian paradigm proceeds, and the various methods that can be used to deal with the computational issues that plague it. This course represents a 70-30 split of practice versus theory.

Content

Learning Prerequisites

Required courses

A master's level understanding of real analysis, linear algebra, statistics and of probability theory is required for this course.

Learning Outcomes

By the end of the course, the student must be able to:

  • Formulate a Bayesian model to tackle a new problem.
  • Identify the limits of how a model accounts for a given dataset.
  • Propose one (or more) approximation method for the model.
  • Implement the methods presented in the course.
  • Recognize how the various methods compare to one another.

Teaching methods

Lecture ex cathedra, exercises in class, homework

Expected student activities

Evaluation is based on a programming project.

Assessment methods

Evaluation is based on a programming project.

Dans le cas de l’art. 3 al. 5 du Règlement de section, l’enseignant décide de la forme de l’examen qu’il communique aux étudiants concernés.

Supervision

Office hours No
Assistants Yes
Forum No

Resources

Bibliography

C. Bishop, Pattern Recognition and Machine Learning

K. Murphy, Machine Learning: A Probabilistic Perspective

C. Robert, The Bayesian choice

Ressources en bibliothèque

In the programs

  • Semester: Spring
  • Exam form: Oral (summer session)
  • Subject examined: Bayesian Computation
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Oral (summer session)
  • Subject examined: Bayesian Computation
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Oral (summer session)
  • Subject examined: Bayesian Computation
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Semester: Spring
  • Exam form: Oral (summer session)
  • Subject examined: Bayesian Computation
  • Lecture: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks

Reference week

 MoTuWeThFr
8-9     
9-10     
10-11     
11-12     
12-13     
13-14     
14-15     
15-16     
16-17     
17-18     
18-19     
19-20     
20-21     
21-22