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. Trees
4. Bagging and Random forests
5. Boosting
6. Variable selection
7. ROC
8. Bayes
9. Gaussian processes
10. State space models
11. Hidden Markov Models

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.