DD2447 Statistical Methods in Applied Computer Science 6.0 credits

Statistiska metoder i datalogin

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

  • Educational level

    Second cycle
  • Academic level (A-D)

    D
  • Subject area

    Computer Science and Engineering
  • Grade scale

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

Course offerings

Autumn 17 statmet17 for programme students

Autumn 17 statmet17SAP for single courses students - To application

  • Periods

    Autumn 17 P2 (6.0 credits)

  • Application code

    10105

  • Start date

    30/10/2017

  • End date

    2018 week: 3

  • Language of instruction

    English

  • Campus

    KTH Campus

  • Number of lectures

    12 (preliminary)

  • Number of exercises

  • Tutoring time

    Daytime

  • Form of study

    Normal

  • Number of places

    No limitation

  • Course responsible

    Jens Lagergren <jensl@kth.se>

  • Target group

    Single course students within SAP

  • Application

    Apply for this course at antagning.se through this application link.
    Please note that you need to log in at antagning.se to finalize your application.

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, grade scale: A, B, C, D, E, FX, F

Requirements for final grade

Assignments and a project (INL1; 6 university credits)

Offered by

CSC/computational Science and Technology

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: Autumn 2016.
Examination information valid from: Autumn 2007.