Machine Learning (2024)

Course registration

Register for the lecture and tutorials via Zeus. You will be automatically added to the ILIAS folder.

Content

  1. Overview of statistical learning
  2. Linear Regression
  3. Resampling Methods
  4. Model Selection and Regularization
  5. Tree-Based Methods
  6. Classification
  7. Bayesian Statistics
  8. Text Mining

Literature

James, Witten, Hastie, Tibshirani (2013/2021).

  • An Introduction to Statistical Learning, with Applications in R. Springer.
    (Some of the figures in these slides are taken from the book with permission from the authors.)
     
  • Hastie, Tibshirani, Friedman (2009). Elements of Statistical Learning. Springer.
     
  • Additional papers which we discuss during the course.

Form of Assessment

The final grade is based on the Final Exam (100%).

Lecture & Tutorial Dates

Lectures:

Time: Wednesday, 17:00 - 18:30

            Thursday, 10:00 – 11:30, fortnightly (begins 11.04.24)

Room: Wednesday, F429

              Thursday, F429

Tutorials:

Time: Thursday, 10:00 - 11:30, fortnightly (begins 18.04.24)

Room: F429

All material will be provided via ILIAS. Please register for the tutorials via Zeus.