# TILS8000 Elements of statistical learning (5 cr)

Study level:
0-5
Language:
English
Responsible organisation:
Department of Mathematics and Statistics
Curriculum periods:
2020-2021, 2021-2022, 2022-2023

## Description

##### Content

The course gives an overview of modern statistical methods for mathematically oriented students. The main focus is in prediction models and assessment of their performance in new data. The course starts with linear models and proceeds to additive generalized models and other non-linear models. Smoothing, splines and regularization play an important role. The methods are applied to real and simulated data.

##### Completion methods

Course exam, exercises and practical assignment

##### Assessment details

The grades are determined by the success in the course exam and possibly also by the performance shown in the exercises and the practical assignment.

## Learning outcomes

Student
• knows linear and non-linear methods for regression and classification
• knows approaches for model assessment and model selection
• knows approaches for regularization and smoothing
• knows some methods for unsupervised learning
• can carry-out data-analysis with real data

## Description of prerequisites

probability calculus, linear algebra, vector calculus, programming

## Study materials

Hastie, T., Tibshirani, R. & Friedman, J. (2009). The Elements of Statistical Learning, Second Edition. Springer, Berlin.
Available at https://web.stanford.edu/~hastie/Papers/ESLII.pdf

## Completion methods

### Method 1

Evaluation criteria:
The assessment is based on the success in the course exam and possibly the activity in doing the assignments as well as the performance in the practical work.
Select all marked parts
Parts of the completion methods
x

### Teaching (5 cr)

Type:
Participation in teaching