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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2024/2025

Machine Learning 2

The course is not on the list Without time-table
Code Completion Credits Range Language
BIE-ML2.21 Z,ZK 5 2P+2C English
Garant předmětu:
Lecturer:
Tutor:
Supervisor:
Department of Applied Mathematics
Synopsis:

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.

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 BIE-ML1 is also assumed.

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

Study Objective:

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

Study materials:

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

2. Deep Learning, I. Goodfellow, Y. Bengio, A. Courville, MIT Press 2016, ISBN 978-0262035613

3. Reinforcement learning, Sutton, R. S. and Barto, A. G., MIT Press 2018, ISBN 9780262039246

Note:
Further information:
https://courses.fit.cvut.cz/BIE-ML2/
No time-table has been prepared for this course
The course is a part of the following study plans:
Data valid to 2024-04-11
Aktualizace výše uvedených informací naleznete na adrese https://bilakniha.cvut.cz/en/predmet6671106.html