| Course Unit Code | 460-4128/01 |
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| Number of ECTS Credits Allocated | 4 ECTS credits |
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| Type of Course Unit * | Compulsory |
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| Level of Course Unit * | Second Cycle |
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| Year of Study * | Second Year |
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| Semester when the Course Unit is delivered | Winter 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 | Course succeeds to compulsory courses of previous semester |
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| Name of Lecturer(s) | Personal ID | Name |
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| PLA06 | prof. Ing. Jan Platoš, Ph.D. |
| Summary |
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The students will become familiar with the algorithms and tools for big data analysis and its real-world applications. First, the core algorithms and tools for big data will be presented as well as its requirements, results and the representations of the outcomes. Later, methods based on the deep neural networks will be described and implemented on a real-world data and real-world computation hardware. Finally, a recomender systems will be introduced and its implementation discussed in details with a demonstration in the lab on real data.
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| Learning Outcomes of the Course Unit |
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The subject aims to teach students with advanced methods of data analysis, especially with work with big data. Therefore, a focus will be placed on efficient algorithms using optimized data structures. The students will demonstrate the knowledge on a practical data analysis and their processing with results and their presentation.
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| Course Contents |
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Lectures:
1. Big data
2. Sampling methods
3. Dimension Reduction algorithms
4. Aggregation and clustering on big data
5. Advanced algorithm for data classification
6. Ensemble classification algorithms
7. Deep Models, Deep Neural Networks
8. Deep model learning algorithms
9. Recommender systems
10. Data Visualization
Excercise:
Practical evaluation of the theory on real-world datasets.
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| Recommended or Required Reading |
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| Required Reading: |
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Ian H. Witten, Eibe Frank, Mark A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, Morgan Kaufmann, 2011, ISBN: 978-0123748560
Charu C. Aggarwal, Data Mining - The Text Book, Springer 2015. |
Ian H. Witten, Eibe Frank, Mark A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, Morgan Kaufmann, 2011, ISBN: 978-0123748560
Charu C. Aggarwal, Data Mining - The Text Book, Springer 2015.
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| Recommended Reading: |
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1. Mohammed J. Zaki, Wagner Meira, Jr., Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University Press, May 2014. ISBN: 9780521766333.
2. Jure Leskovec, Anand Rajaraman, David Ullman, Mining of Massive Datasets, 2nd editions, Cambridge University Press, Novemeber 2014, ISBN: 9781107077232, On-line http://infolab.stanford.edu/~ullman/mmds/book.pdf [2014-09-12]
3. Mohammed J. Zaki, Wagner Meira, Jr., Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University Press, May 2014. ISBN: 9780521766333.
4. Jure Leskovec, Anand Rajaraman, David Ullman, Mining of Massive Datasets, 2nd editions, Cambridge University Press, Novemeber 2014, ISBN: 9781107077232, On-line http://infolab.stanford.edu/~ullman/mmds/book.pdf [2014-09-12]
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1. Mohammed J. Zaki, Wagner Meira, Jr., Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University Press, May 2014. ISBN: 9780521766333.
2. Jure Leskovec, Anand Rajaraman, David Ullman, Mining of Massive Datasets, 2nd editions, Cambridge University Press, Novemeber 2014, ISBN: 9781107077232, On-line http://infolab.stanford.edu/~ullman/mmds/book.pdf [2014-09-12]
3. Mohammed J. Zaki, Wagner Meira, Jr., Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University Press, May 2014. ISBN: 9780521766333.
4. Jure Leskovec, Anand Rajaraman, David Ullman, Mining of Massive Datasets, 2nd editions, Cambridge University Press, Novemeber 2014, ISBN: 9781107077232, On-line http://infolab.stanford.edu/~ullman/mmds/book.pdf [2014-09-12]
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| 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 |