Syllabus for Statistical Machine Learning

Statistisk maskininlärning

A revised version of the syllabus is available.

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

    Explanation of codes

    The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:

    First cycle

    • G1N: has only upper-secondary level entry requirements
    • G1F: has less than 60 credits in first-cycle course/s as entry requirements
    • G1E: contains specially designed degree project for Higher Education Diploma
    • G2F: has at least 60 credits in first-cycle course/s as entry requirements
    • G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
    • GXX: in-depth level of the course cannot be classified

    Second cycle

    • A1N: has only first-cycle course/s as entry requirements
    • A1F: has second-cycle course/s as entry requirements
    • A1E: contains degree project for Master of Arts/Master of Science (60 credits)
    • A2E: contains degree project for Master of Arts/Master of Science (120 credits)
    • AXX: in-depth level of the course cannot be classified

  • Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
  • Established: 2016-03-08
  • Established by: The Faculty Board of Science and Technology
  • Applies from: Autumn 2016
  • Entry requirements:

    120 credits including Probability and Statistics, Linear Algebra II, Single Variable Calculus and a course in introductory programming.

  • Responsible department: Department of Information Technology

Learning outcomes

Students who pass the course 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 the basic ideas of Bayesian modelling and be able to use them for classification and regression.
  • Explain how the quality of a model can be evaluated by use of cross validation, and specifically the trade-off between bias and variance.
  • Explain the challenges with high dimensional data and have a basic understanding of dimensionality reduction.
  • Use principal component analysis and clustering to visualize data and find groupings in data.

Content

This is an introductory course in statistical machine learning, focusing on classification and regression. Linear regression (traditional and Bayesian), classification via logistic regression, linear discriminant analysis, Gaussian processes, kernel methods, cross validation, model selection, regularization (ridge regression and LASSO), regression and classification trees, principal component analysis and clustering. Applying the methods to real data.

Instruction

Lectures, problem solving sessions (both with and without computer) and homework assignments.

Assessment

Written exam (4 credits) and homework assignments (1 credit).

Syllabus Revisions

Reading list

Reading list

Applies from: Autumn 2016

Some titles may be available electronically through the University library.

  • 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

Print syllabus and reading list
Print