Machine Learning Circuit

Subcircuits:

• Machine Learning
• Knowledge Mining

Machine Learning

Annotation:
Machine learning algorithms allow computers to extract an unknown function from data without the need for explicit programming. The candidate will be introduced to basic machine learning methods and then focus on technical portions selected from the literature by agreement with the examiner.

Syllabus:
Basic machine learning methods and the relationships between them. Advanced deep learning methods. Non-parametric methods. Generative models. Reinforcement learning. Specialized learning techniques for text, image, time series processing, etc.

Basic study material :
Probabilistic Machine Learning: An Introduction. Kevin P. Murphy. MIT Press, 2022.

Examiner: doc. RNDr. Tomáš Brázdil, Ph.D., doc. Mgr. Bc. Vít Nováček, PhD

Other recommended reading:
Machine Learning. The Art and Science of Algorithms that Make Sense of Data Peter Flach, Cambridge University Press, 2012.
A First Course in Machine Learning. Simon Rogers and Mark Girolami. Chapman and Hall/CRC, 2016

Knowledge Mining

Annotation:
The candidate will learn about the process of knowledge mining from data, methods of preprocessing and mining from data (Chapters 3, 6, 8, 10 from Han's monograph, 3rd edition). Then he/she will concentrate on selected parts, usually corresponding to the focus of the PhD thesis (three more chapters from Han's monograph or from other study literature as agreed with the examiner).

Outline:
The process of knowledge acquisition. Models. Methods of data preprocessing, including textual methods. Mining from data (including multirelational, network and graph and spatio-temporal). Learning frequent patterns and association rules. Mining from text and the web (text and web mining). Methods for visual data analysis (visual analytics).

Basic study material:
Data mining: concepts and techniques / Jiawei Han, Micheline Kamber, Jian Pei. -- 3rd ed. Morgan Kaufmann 2011.

Examiner: doc. Mgr. Bc. Vít Nováček, PhD, doc. RNDr. Lubomír Popelínský, Ph.D.

Other recommended reading:
Handbook of data visualization / Chun-houh Chen, Wolfgang Härdle, Antony Unwin, editors.. -- Berlin : Springer, c2008
Web data mining : exploring hyperlinks, content, and usage data / Bing Liu.. -- Berlin : Springer, c2007
Review articles from Data Mining and Knowledge Discovery (Springer) and other comparable journals (mainly ACM, IEEE, Springer)