SubjectsSubjects(version: 946)
Course, academic year 2023/2024
  
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: 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-ML2/
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 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.
Aim of the course -
Last update: Lankaš Filip doc. Ing. Ph.D. (21.02.2023)

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

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

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/

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

Further information:

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

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

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

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

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.

 
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