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

Machine Learning 2

Login to KOS for course enrollment Display time-table
Code Completion Credits Range Language
BI-ML2.21 Z,ZK 5 2P+2C Czech
Garant předmětu:
Daniel Vašata
Lecturer:
Daniel Vašata
Tutor:
Daniel Vašata
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. 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.

Note:
Further information:
https://courses.fit.cvut.cz/BI-ML2/
Time-table for winter semester 2023/2024:
Time-table is not available yet
Time-table for summer 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
Mon
Tue
Wed
roomTK:BS
Vašata D.
12:45–14:15
(lecture parallel1)
Dejvice
NTK Ballingův sál
roomT9:155
Vašata D.
16:15–17:45
(lecture parallel1
parallel nr.101)

Dejvice
Posluchárna
Thu
Fri
The course is a part of the following study plans:
Data valid to 2024-05-23
Aktualizace výše uvedených informací naleznete na adrese https://bilakniha.cvut.cz/en/predmet6576906.html