course-details-portlet

TMA4268

Statistical Learning

Choose study year
Credits 7.5
Level Second degree level
Course start Spring 2022
Duration 1 semester
Language of instruction English and norwegian
Location Trondheim
Examination arrangement Portfolio assessment

About

About the course

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».

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
BBAN4001 7.5 sp Autumn 2020
This course has academic overlap with the course in the table above. If you take overlapping courses, you will receive a credit reduction in the course where you have the lowest grade. If the grades are the same, the reduction will be applied to the course completed most recently.

Subject areas

  • Statistics

Contact information

Course coordinator

Lecturers

Department with academic responsibility

Department of Mathematical Sciences