TMA4268 Statistical Learning, spring 2020
News and Messages
- The compulsory 3 exercise sheet is out. Please read the instructions carefully.
- New deadline for compulsory 2: April 9th, 23.59
- 25.3.2020: The decision on how the course will be assessed is out! You will be given a third compulsory project to be completed individually. The sheet will be handed out after Easter and handed in 4th of May 2020. You should work on the sheet alone. A final character grade will be given, whereas the three compulsory exercises are weighted as 20% - 30% - 50%. In compulsory 2 and 3, you need at least 50% of the points to pass the course, and you need at least 50% of the overall (weighted) points to pass. Details on compulsory 3 can be found here
- 23.3.2020: Question 2c in Compulsory 2 was clarified. A new version is uploaded.
Lecturer(s): Stefanie Muff (coordinator), Thiago Martins (NTNU/AIAscience) and Andreas Strand (NTNU)
All the course and learning material will be presented on this course page.
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).