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
Questions for the Exam pdf.
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.
The Bonus Programme: 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
- Jimmy Olsson: random forests
- 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
of the department of mathematics at KTH. Information about the location of the exam is found
Information about the students' office, exam registration etc, can be found
Timo Koski examiner,
To Mathematical Statistics
To Mathematical Statistics Courses