Description

The lecture deals with theoretical and practical concepts from the fields of statistical learning and machine learning. The main focus is on predictive modeling. The weekly tutorial applies these concepts and methods to real examples for illustration purposes. You are expected to work throughthe exercises for the tutorials. They will typically consist of proofs of theory and programming tasks like the implementation of algorithms.
Language and slides are in English. The registration to the moodle course is obligatory.

Course Outline

The topics of the course are:

1. Introduction
2. Learning theory
3. Regression and classification trees
4. Resampling methods
5. Bagging and random forests
6. Boosting
7. Variable selection and regularization
8. Support vector machines
9. Bayes logic
10. Exact Bayes inference
11. Approximate Bayes inference
12. Gaussian processes
13. Bayesian networks

Literature

T. Hastie, R. Tibshirani, J. Friedman. The Elements of Statistical Learning. Springer.
G. James, D. Witten, T. Hastie and R. Tibshirani. An Introduction to Statistical Learning. Springer.
E. Alpaydin. Introduction to Machine Learning. MIT Press.
C. M. Bishop. Pattern Recognition and Machine Learning. Springer.