Subject description - XP36RGM
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Explanatory Notes
Instructions
| XP36RGM | Reading group in data mining and machine learning | ||
|---|---|---|---|
| Roles: | Extent of teaching: | 2P | |
| Department: | 13136 | Language of teaching: | |
| Guarantors: | Kléma J. | Completion: | ZK |
| Lecturers: | Kléma J., Železný F. | Credits: | 4 |
| Tutors: | Kléma J., Železný F. | Semester: | Z,L |
Web page:
https://cw.fel.cvut.cz/wiki/courses/xp36rgm/startAnotation:
Data mining (DM) aims at revealing non-trivial, hidden and ultimately applicable knowledge in large data. Data size and data heterogeneity make two key data mining technical issues to be solved. The main goal is to understand the patterns that drive the processes generating the data. Machine learning (ML) focuses at computer algorithms that can improve automatically through experience and by the use of data. It often puts emphasis on performance that the algorithms reach. The distinction between DM and ML is not strict as machine learning is often used as a means of conducting useful data mining. For this reason, we cover both the areas in the same course. The main goal of the course is to get acquainted with advanced and modern topics in the field.Content:
The course will take a form of reading and discussion group. Each student gives two 1 hour lectures, followed by a 30 min discussion. One of the lectures should be general (book chapters, recent tutorials at major ML/DM conferences, etc.), the second one can present your research (if ML/DM related) or a ML/DM topic that is closely related to your research or research interests. Each student is supposed to read a review paper recommended for the topic before presentations of the other students. It is assumed that students have completed at least some of the master courses on Machine Learning and Data Analysis (B4M36SAN, B4M46SMU, BE4M33SSU).Course outlines:
Exercises outline:
Literature:
| 1. | Rajaraman, A., Leskovec, J., Ullman, J. D.: Mining of Massive Datasets, Cambridge University Press, 2011. | |
| 2. | Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. New York: Springer, 2009. | |
| 3. | Peng, R. D., Matsui, E.: The Art of Data Science. A Guide for Anyone Who Works with Data. Skybrude Consulting, 200, 162, 2015. |
Requirements:
Subject is included into these academic programs:| Program | Branch | Role | Recommended semester |
| Page updated 27.4.2024 17:52:30, semester: Z,L/2023-4, Z/2024-5, Send comments about the content to the Administrators of the Academic Programs | Proposal and Realization: I. Halaška (K336), J. Novák (K336) |