460-4102/01 – Data Analysis IV (MAD IV)

Gurantor departmentDepartment of Computer ScienceCredits4
Subject guarantorprof. RNDr. Václav Snášel, CSc.Subject version guarantorprof. RNDr. Václav Snášel, CSc.
Study levelundergraduate or graduateRequirementChoice-compulsory
Year2Semestersummer
Study languageCzech
Year of introduction2015/2016Year of cancellation
Intended for the facultiesFEIIntended for study typesFollow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
NOW021 Ing. Jana Nowaková, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Graded credit 2+2
Combined Graded credit 10+0

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

Další požadavky na studenta

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

Combined form (validity from: 2015/2016 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Graded credit Graded credit 100  51
Mandatory attendence parzicipation:

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.FormStudy language Tut. centreYearWSType of duty
2019/2020 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology P Czech Ostrava 2 Choice-compulsory study plan
2019/2020 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology K Czech Ostrava 2 Choice-compulsory study plan
2018/2019 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology P Czech Ostrava 2 Choice-compulsory study plan
2018/2019 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology K Czech Ostrava 2 Choice-compulsory study plan
2017/2018 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology P Czech Ostrava 2 Choice-compulsory study plan
2017/2018 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology K Czech Ostrava 2 Choice-compulsory study plan
2016/2017 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology P Czech Ostrava 2 Choice-compulsory study plan
2016/2017 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology K Czech Ostrava 2 Choice-compulsory study plan
2015/2016 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology P Czech Ostrava 2 Choice-compulsory study plan
2015/2016 (N2647) Information and Communication Technology (2612T025) Computer Science and Technology K Czech Ostrava 2 Choice-compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner