KTH Mathematics  


Mathematical Statistics

SF2935 Modern Methods of Statistical Learning

Second Quarter (Lp2) 2017 -2018


  • Short course description
  • Plan for the course in 2017
  • Schedule 2017 from KTH schedule generator
  • Course log and updates
  • Thirty-five Questions for the Exam pdf.

  • Course literature:
    • Textbook:
      An introduction to Statistical Learning, by G. James, D. Witten, T. Hastie, R. Tibshirani. Springer link
    • Text recommended for those with mathematical interests:
      Foundations of Data Science, by Avrim Blum, John Hopcroft and Ravindran Kannan, 2016. pdf

    • Supplementary Reading and Lecturers' Slides.
  • Computer projects:
  • Lecturers (in alphabetic order) & Respective Topics:
    • Timo Koski: multilayer neural networks (ANN) and exponential families, SVM, Bayesian learning, unsupervised learning click.
    • Jimmy Olsson: random forests click.
    • Tetyana Pavlenko: supervised learning, classification, bootstrap click.
  • Guest Lecturers in alphabetic order :
    • Erik Aurell (KTH) click
    • Lukas Käll (KTH) click
    • Sara Väljamets Data Scientist (Analytics) at Klarna Bank AB
  • Teaching assistant (Introduction to R and to computer projects)

    All general administrative information about practicalities of participation in this course, e.g., dates and rooms for exams, application to exams, exam rules, is available via this link to the webpage of the Student affairs office of the Department of Mathematics click



    Important: Students, who are admitted to a course and who intend to attend it, need to activate themselves in Rapp . Log in there using your KTH-id and click on "activate" (aktivera). The codename for sf2935 in Rapp is statin17.
    Students from SU: register at the students' office of the department of mathematics at KTH. Information about the location of the exam is found here. Information about the students' office, exam registration etc, can be found here.
  • Contact: Timo Koski examiner,

To Mathematical Statistics
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Published by: Timo Koski