Machine Learning 1
Code  Completion  Credits  Range  Language 

BIML1.21  Z,ZK  5  2P+2C  Czech 
 Garant předmětu:
 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, Knearest neighbors for classification and regression
4. Linear regression  Ordinary least squares
5. Linear regression  geometrical interpretation, numerical issues
6. Ridge regression, biasvariance tradeoff
7. Logistic regression
8. Ensemble methods (Random forests, Adaboost)
9. Model evaluation, crossvalidation
10. Feature selection
11. Unsupervised learning setup, Association rules
12. Hierarchical clustering, the kmeans algorithm
 Syllabus of tutorials:

1. Introduction, Python and jupyter notebooks
2. Supervised learning setup, Classification setup, Decision trees
3. Regression setup, Knearest neighbors for classification and regression
4. Linear regression  Ordinary least squares
5. Linear regression  geometrical interpretation, numerical issues
6. Ridge regression, biasvariance tradeoff
7. Logistic regression
8. Ensemble methods (Random forests, Adaboost)
9. Model evaluation, crossvalidation
10. Feature selection
11. Unsupervised learning setup, Association rules
12. Hierarchical clustering, the kmeans 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 9781108455145.
2. Alpaydin E. : Introduction to Machine Learning. MIT Press, 2020. ISBN 9780262043793.
3. Murphy K. P. : Machine Learning: A Probabilistic Perspective. MIT Press, 2012. ISBN 9780262018029.
4. Bishop Ch. M. : Pattern Recognition and Machine Learning. Springer, 2006. ISBN 9780387310732.
5. Hastie T., Tibshirani R., Friedman J. : The Elements of Statistical Learning. Springer, 2009. ISBN 9780387848570.
 Note:
 Further information:
 https://courses.fit.cvut.cz/BIML1/
 Timetable 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 Wed Thu Fri  Timetable for summer semester 2024/2025:
 Timetable 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)