Advanced Machine Learning2017HT
No of lectures
12*2 lectures, 4*4 computer labs, 4*2 seminars.
PhD students in Statistics, Computer Science, Applied Mathematics, and related engineering sciences.
The course was last given
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
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.
- 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).
Lectures, computer labs and seminars.
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.
Pattern recognition and machine learning by C.M. Bishop, ISBN 9780387310732.
Gaussian Processes for Machine Learning by Rasmussen and Williams
Mattias Villani and José Pena.
Mattias Villani/José Pena
Lab report and a computer exam.
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