# TMA4268 Statistical Learning, spring 2023

### News Box:

• Important information: The supervision for recommended exercise 11 is online via Whereby. Daesoo is available via the following link: https://whereby.com/daesoo-lee

You can click on the link, allow camera/microphone access, and then you have to "knock". Daesoo will let you in as soon as he is free, but be aware that it might take some time.

• The Faculty of Information Technology and Electrical Engineering (IE) is sending out a questionnaire-based student evaluation for courses taught at IE. Please help us to improve our teaching by answering this survey in the course TMA4268 Statistical Learning: https://nettskjema.no/a/333939 The survey closes on May 5. (You answer anonymously, but need to log in with Feide for safety reasons.)

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: https://mattelab2023v.math.ntnu.no/c/tma4268/6

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).