Machine Learning 2
Code | Completion | Credits | Range | Language |
---|---|---|---|---|
BIE-ML2.21 | Z,ZK | 5 | 2P+2C | English |
- Garant předmětu:
- Lecturer:
- Tutor:
- Supervisor:
- Department of Applied Mathematics
- Synopsis:
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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:
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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:
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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:
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The course aims to introduce students to more advanced methods of a rapidly developing field of machine learning.
- Study materials:
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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:
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- Bachelor specialization, Computer Networks and Internet, 2021 (compulsory elective course)