460-6016/03 – Data Analysis (AD)
| Gurantor department | Department of Computer Science | Credits | 10 |
| Subject guarantor | prof. RNDr. Václav Snášel, CSc. | Subject version guarantor | prof. Ing. Jan Platoš, Ph.D. |
| Study level | postgraduate | Requirement | Choice-compulsory type B |
| Year | | Semester | winter + summer |
| | Study language | Czech |
| Year of introduction | 2019/2020 | Year of cancellation | |
| Intended for the faculties | USP, FEI | Intended for study types | Doctoral |
Subject aims expressed by acquired skills and competences
Goals of the course: data analysis
Teaching methods
Individual consultations
Summary
The content of the subject is following: data reduction methods, machine learning, data pre-processing, xxploratory data analysis, statistical data mining approach, cluster analysis (hierarchical and k-means clustering), Bayesian rules, k-nearest neighbor algorithm, decision trees, factor analysis , self-organizing SOM maps, association and fuzzy rules, rough sets, methods of analyzing multi-dimensional data, time series analysis, PCA, ICA, NMF, SVD, tensor data, tensor reduction, model evaluation, visualization, conceptual unions, knowledge mining from databases.
Compulsory literature:
Trevor Hastie, Robert Tibshirani, Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, 2009.
Claudio Carpineto, Giovanni Romano. Concept Data Analysis: Theory and Applications,Wiley, 2004.
Recommended literature:
Bing Liu. Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data, Springer, 2009.
David Skillicorn. Understanding Complex Datasets: Data Mining with Matrix Decompositions, Chapman & Hall, 2007.
Pang-Ning Tan, Michael Steinbach, Vipin Kumar. Introduction to Data Mining, Addison Wesley, 2005.
Way of continuous check of knowledge in the course of semester
Ongoing review of learning activities and assignments as part of regular
consultations. If the student's assignments also include publishing,
the relevant article will be presented in the course.
E-learning
Other requirements
Additional requirements for the student are not.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
Methods of data reduction, machine learning, data preprocessing, etc.
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
Předmět neobsahuje žádné hodnocení.