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.

  • 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 SAP for Study Abroad Programme (SAP)

  • Periods

    Autumn 18 P2 (7.5 credits)

  • Application code

    10027

  • 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

  • Schedule

    Schedule (new window)

  • Course responsible

    Jens Lagergren <jensl@kth.se>

  • Teacher

    Jens Lagergren <jensl@kth.se>

    Pawel Herman <paherman@kth.se>

  • Target group

    Only open for students within the SAP-programme.

  • 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.

Autumn 18 mladv18 for programme students

Autumn 18 mladv18 for programme students

Autumn 18 mladv18 for programme students

Autumn 18 mladv18 for programme students

Autumn 17 mladv17-SAP for Study Abroad Programme (SAP)

  • Periods

    Autumn 17 P2 (7.5 credits)

  • Application code

    10102

  • Start date

    30/10/2017

  • End date

    15/01/2018

  • 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>

  • Teacher

    Jens Lagergren <jensl@kth.se>

    Pawel Herman <paherman@kth.se>

  • Target group

    Single course students within SAP.

Autumn 17 Doktorand for single courses students

  • Periods

    Autumn 17 P2 (7.5 credits)

  • Application code

    10135

  • Start date

    30/10/2017

  • End date

    15/01/2018

  • Language of instruction

    English

  • Campus

    KTH Campus

  • Tutoring time

    Daytime

  • Form of study

    Normal

  • Number of places *

    Max. 1

    *) If there are more applicants than number of places selection will be made.

  • Course responsible

    Jens Lagergren <jensl@kth.se>

  • Teacher

    Jens Lagergren <jensl@kth.se>

    Pawel Herman <paherman@kth.se>

  • Target group

    For doctoral students at KTH.

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

Disposition

12 lectures

5 exercises

Eligibility

DD2431 Machine learning or the equivalent. SF1901 Probability Theory and

statistics or the equivalent.

Literature

 "Pattern recognition and Machine Learning", Christopher Bishop

Examination

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

Offered by

CSC/computational Science and Technology

Contact

Jens Lagergren (jensl@kth.se)

Examiner

Jens Lagergren <jensl@kth.se>

Supplementary information

Grading criteria are made available when the course starts.

Version

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