Description:

The course focuses on algorithms used in the fields of machine learning and data mining. However, this is not an introductory course, and the students should know machine learning basics. The emphasis is put on advanced algorithms (e.g., gradient boosting) and non-basic kinds of machine learning tasks (e.g., recommendation systems) and models (e.g., kernel methods).

Contents:

1. Recalling basic data mining methods and their applications.
2. Model evaluation.
3. Bias-variance decomposition, negative correlation learning.
4. Decision trees and ensemble methods based on them.
5.-6. (2) Boosting and gradient boosting (XGBoost).
7. Introduction to kernel methods.
8. Kernel methods.
9. Modern kernel methods.
10. - 11. (2) Introduction to recommendation systems, usage of kNN.
12. Matrix factorisation for reccomendation.
13. Hyperparameters tuning, AutoML, new trends.

Seminar contents:

(1-6) Various topics and in-depth examples of model evaluation techniques and selected algorithms

Recommended literature:

1. Hastie, T. - Tibshirani, R. - Friedman, J. : The Elements of Statistical Learning, Data Mining, Inference and Prediction. Springer, 2011. ISBN 978-0387848570.
2. Murphy, K. P. : Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series). MIT Press, 2012. ISBN 978-0262018029.
3. Shai Shalev-Shwartz, Shai Ben-David : Understanding Machine Learning, From Theory to Algorithms. Cambridge University Press, 2014. ISBN 978-1107057135.
4. Aggarwal, Ch. C. : Recommender Systems. Springer, 2016. ISBN 978-3319296579.

Keywords:

Data Mining, Decision Trees, SVM, Neural Networks

Abbreviations used:

Semester:

• W ... winter semester (usually October - February)
• S ... spring semester (usually March - June)
• W,S ... both semesters

Mode of completion of the course:

• A ... Assessment (no grade is given to this course but credits are awarded. You will receive only P (Passed) of F (Failed) and number of credits)
• GA ... Graded Assessment (a grade is awarded for this course)
• EX ... Examination (a grade is awarded for this course)
• A, EX ... Examination (the award of Assessment is a precondition for taking the Examination in the given subject, a grade is awarded for this course)

Weekly load (hours per week):

• P ... lecture
• C ... seminar
• L ... laboratory
• R ... proseminar
• S ... seminar