TMA4268 - Statistical Learning
Statistical learning, multiple linear regression, classification, resampling methods, modell selection/regularization, non-linearity, tree-based methods, neural networks.
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 knows, based on an existing data set, how to choose a suitable statistical model, apply sound statistical methods, and perform the analyses using statistical software. The student knows how to present the results from the statistical analyses, and which conclusions can be drawn from the analyses.
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.
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».
Exam registration requires that class registration is approved in the same semester. Compulsory activities from previous semester may be approved by the department.
Recommended previous knowledge
The course is based on TMA4240/4245 Statistics, or equivalent. Good understanding of linear algebra (matrix methods) and optimization.
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.
Examination arrangement: Portfolio assessment
|Term||Statuskode||Evaluation form||Digital exam||Weighting||Examination aids||Date||Time||Room *|
- * The location (room) for a written examination is published 3 days before examination date.