Course for international guest/part time students
- Faculty
- Faculty of Science
- Organization
- TTK Department of the Physics of Complex Systems
- Code
- dsminingf20vm
- Title
- Data Mining and Machine Learning
- Usual semester
- Autumn
- Published semester
- 2026/27/1
- ECTS
- 6
- Language
- en
- Learning outcomes
- The purpose of the course is to give a theoretical and practical knowledge of techniques from the field of modern data mining and machine learning that are applicable at any quantitative fields science. The primary focus of the course is empirical methods of data driven research, hence it complements knowdledge on model-based physics. a) Knowledge: He/she has an overview of the importance of data analysis of processes, systems, and scientific problems of physics. He/she is aware of the current possibilities, development directions and limits of modern methods of data analysis. b) Abilities: Able to recognize the physical principles of natural phenomena, analyse related data and interpret the results and compare to theoretical expectations. c) Attitude: He/she is constantly striving to expand his/her knowledge and acquire new skills. d) Autonomy and responsibility: He/she is aware of the importance of scientific thinking and accurate conception, and he/she formulates his/her opinion taking these into account.
- Course content
- 1. Introduction, prediction, training set 2. Toolsets of data mining and machine learning 3. Data exploration 4. Supervised learning, objective functions, classification, regression, validation 5. Regularization, model optimization 6. Decision trees, random forests 7. Support Vector Machine 8. Artificial neural networks 9. Deep learning 10. Image and sound processing 11. Unsupervised learning, dimensionality reduction, clustering 12. Machine learning on big data and distributed systems
- Assessment method
- Regular assignments and a written report for a semester project.
- Bibliography
- • Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani: An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) , Springer 2016 • Trevor Hastie, Robert Tibshirani, Jerome Friedman: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, 2009 • Ian Goodfellow, Yoshua Bengio, Aaron Courville: Deep Learning (Adaptive Computation and Machine Learning series), MIT Press, 2016