DD2434 Machine Learning, Advanced Course 7.5 credits

Maskininlärning, avancerad kurs

A second course in machine learning, giving a broadened and deepened introduction to the area.

  • Educational level

    Second cycle
  • Academic level (A-D)

  • Subject area

    Computer Science and Engineering
  • Grade scale

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

Course offerings

Autumn 17 mladv17 for programme students

Autumn 17 mladv17-SAP for single courses students - To application

  • Periods

    Autumn 17 P2 (7.5 credits)

  • Application code


  • Start date


  • End date

    2018 week: 3

  • Language of instruction


  • Campus

    KTH Campus

  • Number of lectures

    12 (preliminary)

  • Number of exercises

    5 (preliminary)

  • Tutoring time


  • Form of study


  • Number of places

    No limitation

  • Course responsible

    Jens Lagergren <jensl@kth.se>

  • Teacher

    Hedvig Kjellström <hedvig@kth.se>

    Jens Lagergren <jensl@kth.se>

    Pawel Herman <paherman@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

Fundamentals of  the probabilistic approach

  • Linear regression 
  • Kernels 
  • Gaussian processes
  • Representation learning
  • Graphical models
  • Hidden Markov Models
  • Expectation-Maximization
  • Variational Inference


12 lectures

5 exercises


DD2431 Machine learning or the equivalent. SF1901 Probability Theory and

statistics or the equivalent.


 "Pattern recognition and Machine Learning", Christopher Bishop

Required equipment


  • LAB1 - Labs, 4.0, grade scale: A, B, C, D, E, FX, F
  • TEN1 - Exam, 3.5, grade scale: A, B, C, D, E, FX, F

Requirements for final grade

Offered by

CSC/computational Science and Technology


Jens Lagergren (jensl@kth.se)


Jens Lagergren <jensl@kth.se>

Supplementary information

Grading criteria are made available when the course starts.


Course syllabus valid from: Autumn 2017.
Examination information valid from: Autumn 2014.