DD2434 Machine Learning, Advanced Course 7.5 credits

Maskininlärning, avancerad kurs

Please note

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

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.

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 19 mladv19 for programme students

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

EECS/Intelligent Systems

Contact

Jens Lagergren (jensl@kth.se)

Examiner

Jens Lagergren <jensl@kth.se>

Pawel Herman <paherman@kth.se>

Supplementary information

Grading criteria are made available when the course starts.

Version

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