FDD3447 Statistical Methods in Applied Computer Science 6.0 credits

Statistiska metoder i datalogi

Offering and execution

Course offering missing for current semester as well as for previous and coming semesters

Course information

Content and learning outcomes

Course contents *

Basic statistical concepts and basic probability theory.

Generative models.

Bayesian inference.

Directed graphical models.

Undirected graphical models.

Exactly inference for graphical models.

State space models.

Particle filters.

Monte Carlo estimation.

Sequential Monte Carlo.

Markov Chain Monte Carlo.

Clustering.

The Dirichlet process.

Intended learning outcomes *

The student should, on completion of the course, be able to:

explain and justify several important machine learning methods,

account for a number of types of methods and algorithms that are used in the field and implement them by means of the book, as well as expand and modify them

evaluate the application of the methods in new contexts critically and design new applications, follow research and development in the area.

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. Furthermore, English B or the equivalent is required.

Recommended prerequisites

No information inserted

Equipment

No information inserted

Literature

Prescribed book and articles.

Examination and completion

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

Grading scale *

P, F

Examination *

    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.

    Examination takes place in the form of homework and project.

    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

    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

    No information inserted

    Offered by

    EECS/Computational Science and Technology

    Main field of study *

    No information inserted

    Education cycle *

    Third cycle

    Add-on studies

    No information inserted

    Postgraduate course

    Postgraduate courses at EECS/Computational Science and Technology