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DD2447 Statistical Methods in Applied Computer Science 6.0 credits

Statistiska metoder i datalogin

Please note

The information on this page is based on a course syllabus that is not yet valid.

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

  • Education cycle

    Second cycle

  • Main field of study

    Computer Science and Engineering

  • Grading scale

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

Course offerings

Autumn 18 statmet18 for programme students

Autumn 18 SAP for Study Abroad Programme (SAP)

  • Periods

    Autumn 18 P2 (6.0 credits)

  • Application code

    10070

  • Start date

    29/10/2018

  • End date

    14/01/2019

  • Language of instruction

    English

  • Campus

    KTH Campus

  • Tutoring time

    Daytime

  • Form of study

    Normal

  • Number of places

    No limitation

  • Course responsible

    Jens Lagergren <jensl@kth.se>

  • Target group

    Only open for students within the SAP-programme.

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 main content

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.

Eligibility

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).

Literature

Machine Learning A Probabilistic Perspective av Kevin P. Murphy.

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

Examination

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

Requirements for final grade

Assignments and a project (INL1; 6 university credits)

Offered by

EECS/Intelligent Systems

Contact

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

Examiner

Jens Lagergren <jensl@kth.se>

Add-on studies

Please discuss with the course leader.

Version

Course syllabus valid from: Spring 2019.
Examination information valid from: Spring 2019.