TMA4268 Statistical Learning, spring 2022

News Box:

  • Next event: The exam on June 3, 9-13. See "Exam information" tab to the left.

Course times: Mondays 14:15-16:00 and Thursdays 08:15-10:00

Exercise sessions: We offer two slots each week: Monday 08:15-10:00 and Wednesday 14:15-16:00.

Lecturer(s): Stefanie Muff (coordinator), Thiago Martins (NTNU/AIAscience), David Klindt (NTNU/Meta)

Teaching Assistants: Emma Skarstein and Daesoo Lee.

Course material: All the course and learning material will be presented on this course page.

In addition we will use Blackboard for handing in compulsory exercises (two in total).

Discussion forum:

Recommended previous knowledge:

  • The course is based on TMA4240/4245 Statistics, or equivalent.
  • Good knowledge of matrix algebra and understanding of optimization.
  • Good understanding of programming - strong emphasis on R programming (you will learn R in this course).

These are the targeted learning outcomes

1. Knowledge. The student has knowledge about the most popular statistical learning models and methods that are used for prediction and inference in science and technology. Emphasis is on regression- and classification-type statistical models.

2. Skills. The student can, based on an existing data set, choose a suitable statistical model, apply sound statistical methods, and perform the analyses using statistical software. The student can present, interpret and communicate the results from the statistical analyses, and knows which conclusions can be drawn from the analyses, and what are the caveats.

Organization of the course

A description of the course organization can be found in the slides of Module 1. Here is the link to the (tentative) course schedule:

Course schedule 2022 (may be modified slightly during the semester).

2022-11-23, Stefanie Muff