TILS8000 Elements of statistical learning (5 cr)

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Study level:
Advanced studies
Grading scale:
0-5
Language:
English
Responsible organisation:
Department of Mathematics and Statistics
Curriculum periods:
2024-2025, 2025-2026, 2026-2027, 2027-2028

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

After successful completion of the course, students will
  • know linear and non-linear methods for regression and classification
  • know approaches for model assessment and model selection
  • know approaches for regularization and smoothing
  • know some methods for unsupervised learning
  • be able to 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

Literature

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
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Teaching (5 cr)

Type:
Participation in teaching
Grading scale:
0-5
Language:
English

Teaching