Description:

The goal of this course is to introduce students to the basic methods of machine learning. They get theoretical understanding and practical working knowledge of regression and classification models in the supervised learning scenario and clustering models in the unsupervised scenario. Students will be aware of the relationships between model bias and variance, and know the fundamentals of assessing model quality. Moreover, they learn the basic techniques of data preprocessing and multidimensional data visualization. In practical demonstrations, pandas and scikit libraries in Python will be used.

Contents:

1. Introduction and basic concepts of Machine Learning
2. Supervised learning setup, Linear regression - Ordinary least squares
3. Linear regression - geometrical interpretation, numerical issues
4. Ridge regression, bias-variance trade-off
5. Classification setup, Decision trees
6. Ensemble methods (Random forests, Adaboost)
7. K-nearest neighbors for classification and regression
8. Logistic regression
9. Model evaluation, cross-validation
10. Feature selection
11. Unsupervised learning setup, Association rules
12. Hierarchical clustering, the k-means algorithm

Seminar contents:

1. Introduction, Python and jupyter notebooks
2. Supervised learning setup, Linear regression - Ordinary least squares
3. Linear regression - geometrical interpretation, numerical issues
4. Ridge regression, bias-variance trade-off
5. Classification setup, Decision trees
6. Ensemble methods (Random forests, Adaboost)
7. K-nearest neighbors for classification and regression
8. Logistic regression
9. Model evaluation, cross-validation
10. Feature selection
11. Unsupervised learning setup, Association rules
12. Hierarchical clustering, the k-means algorithm

Recommended literature:

1. Deisenroth M. P. : Mathematics for Machine Learning. Cambridge University Press, 2020. ISBN 978-1108455145.
2. Alpaydin E. : Introduction to Machine Learning. MIT Press, 2020. ISBN 978-0262043793.
3. Murphy K. P. : Machine Learning: A Probabilistic Perspective. MIT Press, 2012. ISBN 978-0-262-01802-9.
4. Bishop Ch. M. : Pattern Recognition and Machine Learning. Springer, 2006. ISBN 978-0387-31073-2.
5. Hastie T., Tibshirani R., Friedman J. : The Elements of Statistical Learning. Springer, 2009. ISBN 978-0-387-84857-0.

Keywords:

Machine learning, exploratory data analysis, classification, regression, cluster analysis, ensemble classifiers, model evaluation

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