Syllabus for Statistical Machine Learning

Statistisk maskininlärning

Syllabus

  • 5 credits
  • Course code: 1RT700
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Technology A1N, Image Analysis and Machine Learning A1N, Mathematics A1N, Computer Science A1N, Data Science A1N
  • Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
  • Established: 2016-03-08
  • Established by:
  • Revised: 2019-10-29
  • Revised by: The Faculty Board of Science and Technology
  • Applies from: week 27, 2020
  • Entry requirements: 120 credits including Probability and Statistics, Linear Algebra II, Single Variable Calculus and a course in introductory programming.
    English language proficiency that corresponds to English studies at upper secondary (high school) level in Sweden ("English 6").
  • Responsible department: Department of Information Technology

Learning outcomes

On completion of the course, the student should be able to:

  • Structure and divide statistical learning problems into tractable sub-problems, formulate a mathematical solution to the problems and implement this solution using statistical software.
  • Use and develop linear and nonlinear models for classification and regression.
  • Describe the limitations of linear models and understand how these limitations can be handled using nonlinear models.
  • Explain how the quality of a model can be evaluated and how model selection and tuning can be done using cross validation.
  • Explain the trade-off between bias and variance.
  • Describe the difference between parametric and nonparametric models.

Content

This is an introductory course in statistical machine learning, focusing on classification and regression: linear regression, regularization (ridge regression and LASSO), classification via logistic regression, linear discriminant analysis, classification and regression trees, boosting, neural networks, deep learning; practical considerations such as cross validation, model selection, the bias-variance trade off, applying the methods to real data.

Instruction

Lectures, problem solving sessions (both with and without computer), laboratory work, feedback on written assignments.

Assessment

Written exam combined with oral and written presentation of assignments. 
 
If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.

Reading list

Reading list

Applies from: week 27, 2020

  • An introduction to statistical learning : with applications in R James, Gareth; James, Gareth; Witten, Daniela; Hastie, Trevor; Tibshirani, Robert

    New York, NY: Springer, 2013

    Find in the library