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Advanced Machine Learning

2016HT

Status Running - no longer open for registrations
School Computer and Information Science (CIS)
Division STIMA
Owner Mattias Villani
Homepage https://www.ida.liu.se/~732A96/info/courseinfo.en.shtml

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Course plan

No of lectures

Still to be decided, but approximately 5-10 lectures and 3-4 computer labs.

Recommended for

PhD students in Statistics, Computer Science, Applied Mathematics, and related engineering sciences.

The course was last given

Never before.

Goals

Learning about some commonly used probabilistic machine learning models, such as Bayesian networks, State-space models, and hidden Markov models.

Prerequisites

- Introduction to Machine Learning, 6 hp, or equivalent. It is ok to take this course simultaneously with the Advanced course.
- Bayesian Learning, 6 hp, or equivalent.
- Some knowledge of MCMC methods (similar to what is included in the course Bayesian learning).

Organization

Lectures and computer labs.

Contents

Bayesian networks, State space models, Hidden Markov models.

Literature

Pattern recognition and machine learning by C.M. Bishop, ISBN 9780387310732.

Lecturers

Mattias Villani, Oleg Sysoev and José Pena.

Examiner

Mattias Villani/José Pena

Examination

Lab reports

Credit

6 hp

Comments

This course is also given at the master's programme Statistics and Machine Learning and at the Machine learning and AI profile on the civil engineering programme in Software engineering.


Page responsible: Director of Graduate Studies
Last updated: 2012-05-03