Machine Learning 1
| Code | Completion | Credits | Range | Language |
|---|---|---|---|---|
| BIE-ML1.21 | Z,ZK | 5 | 2P+2C | English |
- Course guarantor:
- Daniel Vašata
- Lecturer:
- Rodrigo Augusto Da Silva Alves, Alexander Kovalenko, Daniel Vašata
- Tutor:
- Rodrigo Augusto Da Silva Alves, Alexander Kovalenko, 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, 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
- Syllabus of tutorials:
-
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
- 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/BIE-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 Wed Thu 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, Computer Science, 2021 (compulsory elective course)