TMA4268 Statistical Learning, spring 2024

News Box:

  • Quiz lecture on Friday, 1. March, 12:15-13:00! Come and test your knowledge for Modules 6 and 7.
  • Check the tab "Exam information". You find the exam from 2023 already now.

Course times/place: Thursday 8.15-10.00 (EL6) and Friday 12.15-14.00 (EL6)

Exercise sessions/place: Thursday 16.15-18.00 (EL6)

Lecturer(s): Stefanie Muff (coordinator), Sara Martino

Teaching Assistants: Daesoo Lee, Kenneth Aase.

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 V2024 Modifications possible!

2024-02-29, Stefanie Muff