Machine Learning 1

Course guarantor:

Daniel Vašata

Lecturer:

Karel Klouda, Daniel Vašata

Tutor:

Karel Klouda, Ivo Petr, Ondřej Tichý, Daniel Vašata

Supervisor:

Department of Applied Mathematics

Synopsis:

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.

Requirements:

The knowledge of calculus, linear algebra and probability theory is assumed.

Syllabus of lectures:

1. Introduction and basic concepts of Machine Learning

2. Supervised learning setup, Classification setup, Decision trees

3. Regression setup, K-nearest neighbors for classification and regression

4. Linear regression - Ordinary least squares

5. Linear regression - geometrical interpretation, numerical issues

6. Ridge regression, bias-variance trade-off

7. Logistic regression

8. Ensemble methods (Random forests, Adaboost)

9. Model evaluation, cross-validation

10. Feature selection

11. Unsupervised learning setup, Association rules

12. Hierarchical clustering, the k-means algorithm

Syllabus of tutorials:

1. Introduction, Python and jupyter notebooks

2. Supervised learning setup, Classification setup, Decision trees

3. Regression setup, K-nearest neighbors for classification and regression

4. Linear regression - Ordinary least squares

5. Linear regression - geometrical interpretation, numerical issues

6. Ridge regression, bias-variance trade-off

7. Logistic regression

8. Ensemble methods (Random forests, Adaboost)

9. Model evaluation, cross-validation

10. Feature selection

11. Unsupervised learning setup, Association rules

12. Hierarchical clustering, the k-means algorithm

Study Objective:

The course aims to introduce students to a rapidly developing field of machine learning.

Study materials:

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.

Note:

Further information:

https://courses.fit.cvut.cz/BI-ML1/

Time-table for winter semester 2024/2025:

	06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon
Tue	roomT9:303 Vašata D. 14:30–16:00 (lecture parallel1 parallel nr.101) Dejvice NBFIT PC ucebnaroomT9:303 Vašata D. 16:15–17:45 (lecture parallel1 parallel nr.102) Dejvice NBFIT PC ucebna
Wed	roomT9:303 Petr I. 07:30–09:00 (lecture parallel1 parallel nr.104) Dejvice NBFIT PC ucebnaroomTK:BS Vašata D. 16:15–17:45 (lecture parallel1) Dejvice NTK Ballingův sál roomT9:303 Petr I. 09:15–10:45 (lecture parallel1 parallel nr.105) Dejvice NBFIT PC ucebnaroomT9:351 Tichý O. 12:45–14:15 (lecture parallel1 parallel nr.106) Dejvice NBFIT PC ucebnaroomT9:351 Tichý O. 14:30–16:00 (lecture parallel1 parallel nr.107) Dejvice NBFIT PC ucebna
Thu	roomTH:A-1142 Vašata D. 07:30–09:00 (lecture parallel1 parallel nr.103) Thákurova 7 (budova FSv) Apple lab
Fri

Time-table for summer semester 2024/2025:

Time-table is not available yet

The course is a part of the following study plans:

• Bachelor Specialization Information Security, in Czech, 2021 (elective course)
• Bachelor Specialization Management Informatics, in Czech, 2021 (elective course)
• Bachelor Specialization Computer Graphics, in Czech, 2021 (elective course)
• Bachelor Specialization Computer Engineering, in Czech, 2021 (elective course)
• Bachelor program, unspecified specialization, in Czech, 2021 (VO)
• Bachelor Specialization Web Engineering, in Czech, 2021 (elective course)
• Bachelor Specialization Artificial Intelligence, in Czech, 2021 (PS)
• Bachelor Specialization Computer Science, in Czech, 2021 (compulsory elective course, elective course)
• Bachelor Specialization Software Engineering, in Czech, 2021 (elective course)
• Bachelor Specialization Computer Systems and Virtualization, in Czech, 2021 (elective course)
• Bachelor Specialization Computer Networks and Internet, in Czech, 2021 (elective course)
• Bachelor Specialization Information Security, in Czech, 2024 (elective course)
• Bachelor program, unspecified specialization, in Czech, 2024 (VO)
• Bachelor Specialization Management Informatics, in Czech, 2024 (elective course)
• Bachelor Specialization Computer Graphics, in Czech, 2024 (elective course)
• Bachelor Specialization Software Engineering, in Czech, 2024 (elective course)
• Bachelor Specialization Web Engineering, in Czech, 2024 (elective course)
• Bachelor Specialization Computer Networks and Internet, in Czech, 2024 (elective course)
• Bachelor Specialization Computer Engineering, in Czech, 2024 (elective course)
• Bachelor Specialization Computer Systems and Virtualization, in Czech, 2024 (elective course)
• Bachelor Specialization Artificial Intelligence, in Czech, 2024 (PS)
• Bachelor Specialization Computer Science, in Czech, 20214 (compulsory elective course, elective course)