TMA4268 Statistical Learning, spring 2023

News Box:

  • Here you will find important news during the semester.

Course times/place: Monday 10.15-12.00 (KJL2) and Thursday 08.15-10.00 (EL3)

Exercise sessions/place: Thursday 10:15-12.00 (KJL2)

Lecturer(s): Stefanie Muff (coordinator), Daesoo Lee

Teaching Assistants: Daesoo Lee, Kenneth Aase and Emma Skarstein.

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 models and methods that are used for prediction in science and technology, with emphasis on regression- og 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 2023 (may be modified slightly during the semester).

2023-01-01, Stefanie Muff