KTH Mathematics  

Mathematical Statistics

SF2935 Modern Methods of Statistical Learning

First Quarter (Lp1) 2018 -2019

  • Supplementary Reading and Lecturers' Slides.

  • The Bonus Programme of Summaries: Questions and Sources

  • 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.
    • TBA
  • Guest Lecturers in alphabetic order :
    • Erik Aurell (KTH) click
    • Lukas Käll (KTH) click
    • Sara Väljamets Data Scientist (Analytics) at Klarna Bank AB
    • additional: TBA
  • 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.
    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
    To Mathematical Statistics Courses
    Published by: Timo Koski