Course contents *

Basic statistical concepts and basic probability theory.

Generative models.

Bayesian inference.

Directed graphical models.

Undirected graphical models.

Exact inference for graphical models.

State space models.

Particle filters.

Monte Carlo estimation.

Sequential Monte Carlo.

Markov Chain Mote Carlo.

Clustering.

The Dirichlet process.

Intended learning outcomes *

After successfully taking this course, a student should be able to:

explain and justify several important machine learning methods,

account for several types of methods and algorithms used in the field, implement them using the book, and extend and modify them,

critically evaluate the methods’ applicability in new contexts and construct new applications,

follow research and development in the area.

Course Disposition

No information inserted

Specific prerequisites *

Single course students: 90 university credits including 45 university credits in Mathematics or Information Technology.

Recommended prerequisites

Courses in mathematics (analysis), programming, computer science and statistics equivalent to obligatory courses on D- or F-programme.
Matlab or similar tool (Octave, R).

Equipment

No information inserted

Literature

Machine Learning A Probabilistic Perspective av Kevin P. Murphy.

Kevin P. Murphy's "Machine Learning A Probabilistic Perspective" 

Grading scale *

A, B, C, D, E, FX, F

Examination *

• INL1 - Assignment, 6.0 credits, Grading scale: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Other requirements for final grade *

Assignments and a project (INL1; 6 university credits)

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

No information inserted

Examiner

Jens Lagergren

Course web

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

Offered by

EECS/Intelligent Systems

Main field of study *

Computer Science and Engineering

Education cycle *

Second cycle

Add-on studies

Please discuss with the course leader.

Contact

Jens Lagergren, e-post: jensl@kth.se

Ethical approach *

• All members of a group are responsible for the group's work.
• In any assessment, every student shall honestly disclose any help received and sources used.
• In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Supplementary information

In this course, the EECS code of honor applies, see:
http://www.kth.se/en/eecs/utbildning/hederskodex