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SF2935 Modern Methods of Statistical Learning 7.5 credits

Moderna metoder för statistisk inlärning

  • Education cycle

    Second cycle

  • Main field of study

    Mathematics

  • Grading scale

    A, B, C, D, E, FX, F

Course offerings

Autumn 19 SAP for Study Abroad Programme (SAP)

  • Periods

    Autumn 19 P1 (7.5 credits)

  • Application code

    10047

  • Start date

    26/08/2019

  • End date

    25/10/2019

  • Language of instruction

    English

  • Campus

    KTH Campus

  • Tutoring time

    Daytime

  • Form of study

    Normal

  • Number of places

    No limitation

  • Course responsible

    Pierre Nyquist <pierren@kth.se>

  • Teacher

    Pierre Nyquist <pierren@kth.se>

  • Target group

    Only for SAP-students. Students from UCAS.

  • 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 SAP for Study Abroad Programme (SAP)

Autumn 18 Doktorand for single courses students

  • Periods

    Autumn 18 P1 (7.5 credits)

  • Application code

    10127

  • Start date

    27/08/2018

  • End date

    26/10/2018

  • 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

    Timo Koski <tjtkoski@kth.se>

  • Teacher

    Timo Koski <tjtkoski@kth.se>

  • Target group

    For doctoral students at KTH

Autumn 18 Doktorand for single courses students CANCELLED

  • Periods

    Autumn 18 P1 (7.5 credits)

  • Application code

    10164

  • Start date

    27/08/2018

  • End date

    26/10/2018

  • 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

    Timo Koski <tjtkoski@kth.se>

  • Teacher

    Timo Koski <tjtkoski@kth.se>

  • Target group

    For doctoral students at KTH

Information for research students about course offerings

2015, period 2 

Intended learning outcomes

This course presents an overview of the most important methods of the modern theory of statistical learning. This course focuses primarily on the practical aspects of statistical learning.Computer-aided project work with a variety of datasets forms the essential learning activity. To pass the course, the student should be able to do the following:

  • explain the difference between unsupervised and supervised learning

  • know the underlying mathematical relationships within and a cross statistical learning algorithms and the paradigms of supervised and unsupervised learning along with their strengths and weaknesses

  • identify the correct statistical tool for a data analysis problem in the real world based on reasoned argument

  • use  algorithmic models treating the data mechanism as unknown

  • develop accurate and informative alternatives to data modelling on big and complex as well as on smaller data sets

  • design and implement various statistical learning algorithms in a range of real-world applications

  • design test procedures in order to evaluate a model, optimise the models learned and report on the expected accuracy that can be achieved by applying the models

  • read current research papers and understand the issues raised by current research.

    To receive the highest grade, the student should in addition be able to do the following:

  • combine several models in order to gain better results.

Course main content

This course presents an overview of the most important methods of the modern theory of statistical learning. Topics covered include supervised learning with a focus on classification methods, support vector machines, artificial neural networks, decision trees, boosting, bagging and methods of unsupervised learning with focus on K-means clustering and nearest neighbours. This course focuses primarily on the practical aspects of statistical learning. Computer-aided project work with a variety of datasets forms the essential learning activity.

Disposition

Lectures, presentations, work with computer-aided data analysis.

Eligibility

Courses in probability and statistics, liner algebra, calculus in one and several variables, numerical methods.

Recommended prerequisites

Calculus in one and several variables,  linear  algebra,   numerical methods,  differential equations,  probability and statistics.

Literature

An introduction to Statistical Learning, by G. James, D. Witten, T. Hastie, R. Tibshirani,  Springer Verlag, and additional reading available on the course web   page.

Examination

  • TENA - Examination, 4.5, grading scale: A, B, C, D, E, FX, F
  • ÖVN1 - Assignments, 3.0, grading scale: P, F

The written exam deals with concepts.

Requirements for final grade

Written exam, assignments.

Offered by

SCI/Mathematics

Contact

Timo Koski (tjtkoski@kth.se)

Examiner

Timo Koski <tjtkoski@kth.se>

Version

Course syllabus valid from: Autumn 2017.
Examination information valid from: Autumn 2015.