Course - Statistical Learning - TMA4268
TMA4268 - Statistical Learning
About
Examination arrangement
Examination arrangement: Portfolio assessment
Grade: Letter grades
| Evaluation | Weighting | Duration | Examination aids |
|---|---|---|---|
| Home examination | 80/100 | 4 hours | |
| Work | 20/100 | ALLE |
Course content
Statistical learning, multiple linear regression, classification, resampling methods, modell selection/regularization, non-linearity, tree-based methods, neural networks.
Learning outcome
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.
Learning methods and activities
Lectures, exercises and works (projects). Portfolio assessment is the basis for the grade awarded in the course. This portfolio comprises a written final examination (80%) and works (projects) (20%). The results for the constituent parts are to be given in %-points, while the grade for the whole portfolio (course grade) is given by the letter grading system. Retake of examination may be given as an oral examination. The lectures may be given in English. If the course is taught in English, the exam may be given only in English. Students are free to choose Norwegian or English for written assessments.
Compulsory assignments
- Work
Further on evaluation
In the case that the student receives an F/Fail as a final grade after both ordinary and re-sit exam, then the student must retake the course in its entirety. Submitted work that counts towards the final grade will also have to be retaken. For more information about grading and evaluation. see «Teaching methods and activities».
Recommended previous knowledge
The course is based on TMA4240/4245 Statistics, or equivalent. Good understanding of linear algebra (matrix methods) and optimization.
Course materials
James, G., Witten, D., Hastie, T., Tibshirani, R. "An Introduction to Statistical Learning with Applications in R", Springer. Additional literature will be announced at the start of the course.
Credit reductions
| Course code | Reduction | From | To |
|---|---|---|---|
| BBAN4001 | 7.5 | AUTUMN 2020 |
Version: 1
Credits:
7.5 SP
Study level: Second degree level
Term no.: 1
Teaching semester: SPRING 2022
Language of instruction: English, Norwegian
Location: Trondheim
- Statistics
Department with academic responsibility
Department of Mathematical Sciences