SubjectsSubjects(version: 947)
Course, academic year 2023/2024
  
Machine Learning I - B500010
Title: Strojové učení I
Guaranteed by: Department of Informatics and Chemistry (143)
Faculty: Faculty of Chemical Technology
Actual: from 2019
Semester: winter
Points: winter s.:5
E-Credits: winter s.:5
Examination process: winter s.:
Hours per week, examination: winter s.:2/2, C+Ex [HT]
Capacity: unlimited / unlimited (unknown)
Min. number of students: unlimited
Language: Czech
Teaching methods: full-time
Teaching methods: full-time
Level:  
For type:  
Additional information: https://courses.fit.cvut.cz/BI-ML1/
Note: course can be enrolled in outside the study plan
enabled for web enrollment
Guarantor: Vašata Daniel Ing. Ph.D.
Interchangeability : B500007
Annotation -
Last update: Lankaš Filip doc. Ing. Ph.D. (21.02.2023)
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.
Aim of the course -
Last update: Lankaš Filip doc. Ing. Ph.D. (21.02.2023)

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

Literature -
Last update: Lankaš Filip doc. Ing. Ph.D. (21.02.2023)

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.

Further information:

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

Learning resources -
Last update: Lankaš Filip doc. Ing. Ph.D. (21.02.2023)

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

Syllabus -
Last update: Lankaš Filip doc. Ing. Ph.D. (21.02.2023)

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

Entry requirements -
Last update: Lankaš Filip doc. Ing. Ph.D. (21.02.2023)

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

 
VŠCHT Praha