Machine Learning II - B500011
Title: Strojové učení II
Guaranteed by: Department of Informatics and Chemistry (143)
Faculty: Faculty of Chemical Technology
Actual: from 2021
Semester: summer
Points: summer s.:5
E-Credits: summer s.:5
Examination process: summer s.:
Hours per week, examination: summer s.:2/2, C+Ex [HT]
Capacity: unknown / unknown (unknown)
Min. number of students: unlimited
State of the course: taught
Language: Czech
Teaching methods: full-time
Teaching methods: full-time
Level:  
Additional information: https://courses.fit.cvut.cz/BI-ML2/
Note: course can be enrolled in outside the study plan
enabled for web enrollment
Guarantor: Vašata Daniel Ing. Ph.D.
Interchangeability : B500007
Examination dates   
Annotation -
The goal of this course is to introduce students to the selected advanced methods of machine learning. In the supervised learning scenario, they, in particular, learn kernel methods and neural networks. In the unsupervised learning scenario students learn the principal component analysis and other dimensionality reduction methods. Moreover, students get the basic principles of reinforcement learning and natural language processing.
Last update: Lankaš Filip (21.02.2023)
Aim of the course -

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

Last update: Lankaš Filip (21.02.2023)
Literature -

1. Hastie T., Tibshirani R., Friedman, J. : The Elements of Statistical Learning. Springer, 2009. ISBN 978-0-387-84857-0.

2. Goodfellow I., Bengio Y., Courville A. : Deep Learning. MIT Press, 2016. ISBN 978-0-262-03561-3.

3. Sutton R. S., Barto A. G. : Reinforcement Learning. MIT Press, 2018. ISBN 978-0-262-03924-6.

Further information:

https://courses.fit.cvut.cz/BI-ML2/

Last update: Lankaš Filip (21.02.2023)
Syllabus -

Syllabus of lectures:

1. Linear basis expansion, Kernel regression

2. Support vector machines for classification

3. Dimensionality reduction - Principal component analysis

4. Dimensionality reduction - Linear discriminant analysis, Locally linear embedding

5. Generative models - Naive Bayes

6. Neural Networks - Perceptron, multi-layer perceptron, deep learning

7. Neural Networks - backpropagation, regularization

8. Neural Networks - convolutional neural networks

9. Neural networks - recurrent neural networks, modern trends

10. Reinforcement learning - introduction, multi-armed bandit

11. Reinforcement learning - Markov decision processes

12. Natural language processing

Syllabus of tutorials:

1. Linear basis expansion, Kernel regression

2. Support vector machines

3. Dimensionality reduction - Principal component analysis

4. Dimensionality reduction - Linear discriminant analysis, Locally linear embedding

5. Generative models - Naive Bayes

6. Neural Networks - Perceptron, multi-layer perceptron

7. Neural Networks - deep learning, regularization

8. Neural Networks - convolutional neural networks

9. Neural networks - recurrent neural networks

10. Reinforcement learning I

11. Reinforcement learning II

12. Natural language processing

Last update: Lankaš Filip (21.02.2023)
Learning resources -

Further information:

https://courses.fit.cvut.cz/BI-ML2/

Last update: Lankaš Filip (21.02.2023)
Entry requirements -

The knowledge of calculus, linear algebra and probability theory is assumed. Furthermore, the knowledge of machine learning corresponding to topics covered in the course B500010 is also assumed.

Last update: Lankaš Filip (21.02.2023)