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About course

Before choosing course

Administrate About course

This course summarizes statistical and probabilistic methods used in applied Computer Science.

Choose semester and course offering

Choose semester and course offering to see information from the correct course syllabus and course offering.

* Retrieved from Course syllabus DD2447 (Autumn 2020–)

Content and learning outcomes

Course contents

  • Generative models.
  • Bayesian inference.
  • Probabilistic programming.
  • Graphical models.
  • Concealed Markov models with continuous states.
  • Particle filters.
  • Monte Carlo estimation.
  • Sequential Monte Carlo.
  • Markov Chain Monte Carlo.
  • Clustering.
  • The Dirichlet process.

Intended learning outcomes

After passing the course, the student should be able to

  • explain and justify several important methods for machine learning
  • give an account of several types of methods and algorithms that are used in the field of sample-based inference methods
  • implement several types of methods and algorithms that are used in the field based on a high-level description
  • extend and modify the methods that the course deals with

in order to be able to make a degree project in sample-based inference methods.

Course Disposition

No information inserted

Literature and preparations

Specific prerequisites

For non-program students, 90 credits are required, of which 45 credits have to be within 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

No information inserted

Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

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

Examination

  • INL1 - Assignment, 6,0 hp, betygsskala: 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

Written assignments and a project (INL1; 6 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

Profile picture Jens Lagergren

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.

Further information

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.

Course web DD2447

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

Supplementary information

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