CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2023/2024
UPOZORNĚNÍ: Jsou dostupné studijní plány pro následující akademický rok.

# Machine Learning 1

Code Completion Credits Range Language
BIE-ML1.21 Z,ZK 5 2P+2C English
Garant předmětu:
Daniel Vašata
Lecturer:
Rodrigo Augusto Da Silva Alves, Daniel Vašata
Tutor:
Rodrigo Augusto Da Silva Alves, 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

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

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 2023/2024:
 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 roomT9:346Da Silva Alves R.09:15–10:45(lecture parallel1)DejviceNBFIT učebnaroomT9:303Da Silva Alves R.11:00–12:30(lecture parallel1parallel nr.101)DejviceNBFIT PC ucebna
Time-table for summer semester 2023/2024:
Time-table is not available yet
The course is a part of the following study plans:
Data valid to 2024-05-30
Aktualizace výše uvedených informací naleznete na adrese https://bilakniha.cvut.cz/en/predmet6627606.html