Course Unit Code | 460-4140/01 |
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Number of ECTS Credits Allocated | 4 ECTS credits |
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Type of Course Unit * | Optional |
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Level of Course Unit * | Second Cycle |
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Year of Study * | First Year |
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Semester when the Course Unit is delivered | Summer Semester |
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Mode of Delivery | Face-to-face |
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Language of Instruction | Czech |
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Prerequisites and Co-Requisites | |
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| Prerequisities | Course Unit Code | Course Unit Title |
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| 460-4139 | Machine Learning |
Name of Lecturer(s) | Personal ID | Name |
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| PLA06 | prof. Ing. Jan Platoš, Ph.D. |
Summary |
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In the course, students will get acquainted with deep learning methods with particular emphasis on deep neural networks. Students build on their knowledge of machine learning and deepen it through demonstrations and a deep learning approach to various data types, from vectors, images, text, or data streams. Students will have the chance to test their knowledge and skills using appropriate tools and libraries over artificial and real data and interpret the results for their complete understanding. |
Learning Outcomes of the Course Unit |
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The course aims to acquaint students with deep learning methods, deep neural networks, and other methods of advanced data processing. Students will be acquainted with basic and advanced deep learning methods and their applications over vector, image, text, and other data. |
Course Contents |
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Lectures (topics):
1. Neural networks, principles, basic properties.
2. Neural networks - parameters.
3. Convolutional neural networks.
4. Autocoder.
5. Variation car encoder.
6. Recurrent neural networks.
7. Analysis of time series using neural networks.
8. Text classification - word representation
9. Language modeling using RNN
10. Vector data processing - Exploratory analysis and classification
11. Locating and recognizing objects in the image
12. Generative methods - GAN
Exercises in the computer room:
1. Neural networks, principles, basic properties.
2. Neural networks - parameters.
3. Convolutional neural networks.
4. Autocoder.
5. Variation car encoder.
6. Recurrent neural networks.
7. Analysis of time series using neural networks.
8. Text classification - word representation
9. Language modeling using RNN
10. Vector data processing - Exploratory analysis and classification
11. Locating and recognizing objects in the image
12. Generative methods - GAN |
Recommended or Required Reading |
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Required Reading: |
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- Slides from Lectures
[1] GOODFELLOW, Ian, Yoshua BENGIO a Aaron COURVILLE. Deep learning. Illustrated edition. Cambridge, MA: MIT press, 2016. Adaptive computation and machine learning series. ISBN 978-0262035613.
[2] SAITOH, Koki. Deep Learning from the Basics: Python and Deep Learning: Theory and Implementation. Birmingham, UK: Packt Publishing, 2021. ISBN 978-1800206137.
[3] GÉRON, Aurélien. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: concepts, tools, and techniques to build intelligent systems. Second edition. Beijing: O'Reilly, 2019. ISBN 978-1-4920-3264-9.
[4] HOWARD, Jeremy a Sylvain GUGGER. Deep learning for coders with Fastai and PyTorch: Ai applications without a PhD. Sebastopol, CA: O´Reilly, 2020. ISBN 978-1-492-04552-6.
[5] KELLEHER, John D. Deep learning. Illustrated edition. Cambridge: The MIT Press, 2019. MIT Press essential knowledge series. ISBN 978-0262537551.
[6] KROHN, Jon, Grant BEYLEVELD a Aglaé BASSENS. Deep learning illustrated: a visual, interactive guide to artificial intelligence. Boston: Addison-Wesley, [2020]. ISBN 978-0135116692. |
- Prezentace k přednáškám
[1] CHOLLET, François. Deep learning v jazyku Python: knihovny Keras, Tensorflow. Přeložil Rudolf PECINOVSKÝ. Praha: Grada Publishing, 2019. Knihovna programátora (Grada). ISBN 978-80-247-3100-1.
[2] GOODFELLOW, Ian, Yoshua BENGIO a Aaron COURVILLE. Deep learning. Illustrated edition. Cambridge, MA: MIT press, 2016. Adaptive computation and machine learning series. ISBN 978-0262035613.
[3] SAITOH, Koki. Deep Learning from the Basics: Python and Deep Learning: Theory and Implementation. Birmingham, UK: Packt Publishing, 2021. ISBN 978-1800206137.
[4] GÉRON, Aurélien. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: concepts, tools, and techniques to build intelligent systems. Second edition. Beijing: O'Reilly, 2019. ISBN 978-1-4920-3264-9.
[5] HOWARD, Jeremy a Sylvain GUGGER. Deep learning for coders with Fastai and PyTorch: Ai applications without a PhD. Sebastopol, CA: O´Reilly, 2020. ISBN 978-1-492-04552-6.
[6] KELLEHER, John D. Deep learning. Illustrated edition. Cambridge: The MIT Press, 2019. MIT Press essential knowledge series. ISBN 978-0262537551.
[7] KROHN, Jon, Grant BEYLEVELD a Aglaé BASSENS. Deep learning illustrated: a visual, interactive guide to artificial intelligence. Boston: Addison-Wesley, [2020]. ISBN 978-0135116692.
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Recommended Reading: |
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[1] AGGARWAL, Charu C. Data mining: the textbook. New York, NY: Springer Science+Business Media, 2015. ISBN 978-3-319-14141-1.
[2] BRAMER, M. A. Principles of data mining. London: Springer, 2007. ISBN 1-84628-765-0.
[3] LESKOVEC, Jure, Anand RAJARAMAN a Jeffrey D. ULLMAN. Mining of massive datasets, Standford University. Second edition. Cambridge: Cambridge University Press, 2014. ISBN 9781107077232.
[4] WITTEN, Ian H., Eibe FRANK, Mark A. HALL a Christopher J. PAL. Data mining: Practical machine learning tools and techniques. Fourth Edition. Amsterdam: Elsevier, [2017]. ISBN 978-0-12-804291-5.
[5] ZAKI, Mohammed J. a Wagner MEIRA JR. Data Mining and Analysis: Fundamental Concepts and Algorithms. 2nd edition. Cambridge, GB: Cambridge University Press, 2020. ISBN 978-0521766333.
[6] LAPAN, Maxim. Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more. 1. Birmingham, UK: Packt Publishing, 2018. ISBN 978-1788839303. |
[1] AGGARWAL, Charu C. Data mining: the textbook. New York, NY: Springer Science+Business Media, 2015. ISBN 978-3-319-14141-1.
[2] BRAMER, M. A. Principles of data mining. London: Springer, 2007. ISBN 1-84628-765-0.
[3] LESKOVEC, Jure, Anand RAJARAMAN a Jeffrey D. ULLMAN. Mining of massive datasets, Standford University. Second edition. Cambridge: Cambridge University Press, 2014. ISBN 9781107077232.
[4] WITTEN, Ian H., Eibe FRANK, Mark A. HALL a Christopher J. PAL. Data mining: Practical machine learning tools and techniques. Fourth Edition. Amsterdam: Elsevier, [2017]. ISBN 978-0-12-804291-5.
[5] ZAKI, Mohammed J. a Wagner MEIRA JR. Data Mining and Analysis: Fundamental Concepts and Algorithms. 2nd edition. Cambridge, GB: Cambridge University Press, 2020. ISBN 978-0521766333.
[6] LAPAN, Maxim. Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more. 1. Birmingham, UK: Packt Publishing, 2018. ISBN 978-1788839303. |
Planned learning activities and teaching methods |
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Lectures, Tutorials |
Assesment methods and criteria |
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Task Title | Task Type | Maximum Number of Points (Act. for Subtasks) | Minimum Number of Points for Task Passing |
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Graded credit | Graded credit | 100 | 51 |