TMA4268 Statistical Learning, spring 2021

News Box:

On Monday 15. March we will have a guest lecture 15:15-16:00. Two people from BearingPoint (www.bearingpoint.com) will give you insight into a company that employs people with a background in applied statistics and machine learning.

We are back to physical teaching on Tuesdays. Starting from 8. March we have enough space for all those who want to attend the class in S8. The lecture will still be hybrid via the usual Tuesday zoom link (including recording).

Modules 6, 7 and 10 (22./23. February, 1./2. March, and 22./23. March) will be taught by Thiago Martins under the following link: https://NTNU.zoom.us/j/93695342168?pwd=UWQ1SjBra1ZTMG9ad2tpUC9nb3F3dz09

Course times: Monday 14:15-16:00 and Tuesday 10:15-12:00 (lecture) and Tuesday 16:15-18:00 (exercises). We will unfortunately have to start the semester as a full online course.

Zoom links:

Monday lectures are accessible via this link: https://NTNU.zoom.us/j/93506041632?pwd=Q3ZvREJJaHdPQVZVaGgrbW1YS3RQZz09
Tuesday lectures are for now also online: https://NTNU.zoom.us/j/97421394324?pwd=bFNQcC9WK0tzcmsxUElxT241WURzQT09

Modules 6, 7 and 10 (22./23. February, 1./2. March, and 22./23. March) will be taught by Thiago Martins under the following link: https://NTNU.zoom.us/j/93695342168?pwd=UWQ1SjBra1ZTMG9ad2tpUC9nb3F3dz09

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

Teaching Assistants: Emma Skarstein and Michail Spitieris.

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). Go directly to TMA4268 on Bb.

Discussion forum: If you have questions regarding the lecture or organization of the course, please post them on the Piazza discussion forum. You can sign up here.

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 TMA4268 (may be modified slightly during the semester).