Reading group in data mining and machine learning
| Code | Completion | Credits | Range |
|---|---|---|---|
| XP36RGM | ZK | 4 | 2P |
- Garant předmětu:
- Jiří Kléma
- Lecturer:
- Jiří Kléma, Filip Železný
- Tutor:
- Jiří Kléma, Filip Železný
- Supervisor:
- Department of Computer Science
- Synopsis:
-
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.
- Requirements:
- Syllabus of lectures:
- Syllabus of tutorials:
- Study Objective:
- Study materials:
-
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.
- Note:
- Further information:
- https://cw.fel.cvut.cz/wiki/courses/xp36rgm/start
- Time-table for winter semester 2024/2025:
-
06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon Tue Wed Thu Fri - Time-table for summer semester 2024/2025:
- Time-table is not available yet
- The course is a part of the following study plans:
-
- Doctoral studies, structured daily studies (compulsory elective course)
- Doctoral studies, structured combined studies (compulsory elective course)