460-4102/02 – Data Analysis IV (MAD IV)
Gurantor department | Department of Computer Science | Credits | 4 |
Subject guarantor | prof. Ing. Jan Platoš, Ph.D. | Subject version guarantor | prof. Ing. Jan Platoš, Ph.D. |
Study level | undergraduate or graduate | Requirement | Choice-compulsory |
Year | 2 | Semester | summer |
| | Study language | English |
Year of introduction | 2015/2016 | Year of cancellation | 2022/2023 |
Intended for the faculties | FEI | Intended for study types | Follow-up Master |
Subject aims expressed by acquired skills and competences
Graduate Course gives the following knowledge and skills:
basic theoretical background for data analysis,
implementation and application of selected methods,
practical application to real data,
application of selected software packed to data analysis,
visualization and analysis results.
Teaching methods
Lectures
Tutorials
Summary
Evaluation and interpretation of information obtained from the measured and recorded data from the practice.
Compulsory literature:
Han Jiawei; Kamber Micheline; Pei Jian, Data Mining, The Morgan Kaufmann Series in Data Management Systems, 3rd edition, 2011.
Mohammed J. Zaki, Wagner Meira. Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press, 2014
Langville, Amy N.; Meyer, Carl D. D. Who's #1?: The Science of Rating and Ranking. Princeton University Press. 2012.
Robinson, Ian; Webber, Jim; Eifrem. Graph Databases. O'Reilly Media. 2013.
Murphy, Kevin P. Machine Learning: A Probabilistic Perspective.The MIT Press. 2013.
Recommended literature:
D. Skillicorn, Understanding Complex datasets: data mining with matrix decompositions, Chapman & Hall/CRC, 2007.
Way of continuous check of knowledge in the course of semester
E-learning
Other requirements
Additional requirements are not placed on the student.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
Machine learning
Bayesian networks
Reinforcement learning
Bagging
Boosting
Stacking
complex network
Ranking
Analysis of tensor data
Graph databases
Data visualization
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction