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Bayesian Learning

2012HT

Status Archive
School Computer and Information Science (CIS)
Division Statistics
Owner Mattias Villani
Homepage https://www.ida.liu.se/~732A46/info/courseinfo.en.shtml

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Course plan

No of lectures

22 lecture hours + 10 exercise hours + 8 computer labs hours

Recommended for

PhD students in Statistics, Computer Science and Cognitive Science.

The course was last given

This is the first the course is given.

Goals

The course aims to give a solid introduction to the Bayesian approach to statistical inference, with a view towards applications in data mining and machine learning.

Prerequisites

Students admitted to the Master’s programme in Statistics and Data Mining fulfill the admission requirements for the course.
Students not admitted to the Masters’ programme in Statistics and Data Mining should have passed:
an intermediate course in probability and statistical inference
a basic course in mathematical analysis
a basic course in linear algebra
a basic course in programming
It also required to have a basic knowledge of linear regression, either as a part of a statistics course, or as a separate course.

Organization

The course consists of lectures, exercise sessions, and computer labs. The lectures are devoted to presentations of concepts and methods. Mathematically oriented problems are solved in the exercise sessions. The computer labs are used for practical applications of Bayesian inference.
Language of instruction: English.
The schedule will be travel-friendly.

Contents

The course aims to give a solid introduction to the Bayesian approach to statistical inference, with a view towards applications in data mining and machine learning. After an introduction to the subjective probability concept that underlies Bayesian inference, the course moves on to the mathematics of the prior-to-posterior updating in basic statistical models, such as the Bernoulli, normal and multinomial models. Linear regression and spline regression are also analyzed using a Bayesian approach. The course subsequently shows how complex models can be analyzed with simulation methods like Markov Chain Monte Carlo (MCMC). Bayesian prediction and marginalization of nuisance parameters is explained, and introductions to Bayesian model selection and Bayesian decision theory are also given.

Literature

To be decided.

Lecturers

Mattias Villani

Examiner

Mattias Villani

Examination

The course is examined by written reports on computer lab assignments and an individual project, which may be complemented with an oral exam.

Credit

6 ECTS credits.

Comments

A more lightweight version of the course is given on the Masters' programme Statistics and Data Mining.


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Last updated: 2012-05-03