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Advanced Machine Learning

2017HT

Status Cancelled
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

12*2 lectures, 4*4 computer labs, 4*2 seminars.

Recommended for

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

The course was last given

Fall 2016.

Goals

The course presents the analysis of several large classes of models widely used in advanced machine learning, such as state-space models, gaussian processes, hidden Markov models, Bayesian networks, and Markov random fields. Students will learn about the structure and learning of these models, when they are applicable, how to use them in practical machine learning applications, and how to correctly interpret the results. The models are mainly analyzed from a Bayesian perspective.
After completing the course the student should be able to:
• use the introduced model classes to accurately formulate and solve practical problems.
• learn the parameters and perform predictions in the presented models.
• evaluate and choose among the models within each class.
• implement the models and learning methods in a programming language.

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, computer labs and seminars.

Contents

Bayesian learning summary, Gaussian processes, State-space models, Kalman filtering and smoothing, Particle methods, Graphical models, Bayesian networks, Markov models, Hidden Markov models, Markov random fields.

Literature

Pattern recognition and machine learning by C.M. Bishop, ISBN 9780387310732.
Gaussian Processes for Machine Learning by Rasmussen and Williams

Lecturers

Mattias Villani and José Pena.

Examiner

Mattias Villani/José Pena

Examination

Lab report and a computer exam.

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