460-4128/02 – Advanced Methods for Data Analysis (PMAD)

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	Compulsory
Year	2	Semester	winter
		Study language	English
Year of introduction	2019/2020	Year of cancellation	2022/2023
Intended for the faculties	FEI	Intended for study types	Follow-up Master

Instruction secured by
Login	Name	Tuitor	Teacher giving lectures
PLA06	prof. Ing. Jan Platoš, Ph.D.

Extent of instruction for forms of study
Form of study	Way of compl.	Extent
Full-time	Graded credit	2+2

Subject aims expressed by acquired skills and competences

The subject aims to teach students with advanced methods of data analysis, especially with work with big data. Therefore, a focus will be placed on efficient algorithms using optimized data structures. The students will demonstrate the knowledge on a practical data analysis and their processing with results and their presentation.

Teaching methods

Lectures
Tutorials

Summary

The students will become familiar with the algorithms and tools for big data analysis and its real-world applications. First, the core algorithms and tools for big data will be presented as well as its requirements, results and the representations of the outcomes. Later, methods based on the deep neural networks will be described and implemented on a real-world data and real-world computation hardware. Finally, a recomender systems will be introduced and its implementation discussed in details with a demonstration in the lab on real data.

Compulsory literature:

Ian H. Witten, Eibe Frank, Mark A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, Morgan Kaufmann, 2011, ISBN: 978-0123748560 Charu C. Aggarwal, Data Mining - The Text Book, Springer 2015.

Recommended literature:

1. Mohammed J. Zaki, Wagner Meira, Jr., Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University Press, May 2014. ISBN: 9780521766333. 2. Jure Leskovec, Anand Rajaraman, David Ullman, Mining of Massive Datasets, 2nd editions, Cambridge University Press, Novemeber 2014, ISBN: 9781107077232, On-line http://infolab.stanford.edu/~ullman/mmds/book.pdf [2014-09-12] 3. Mohammed J. Zaki, Wagner Meira, Jr., Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University Press, May 2014. ISBN: 9780521766333. 4. Jure Leskovec, Anand Rajaraman, David Ullman, Mining of Massive Datasets, 2nd editions, Cambridge University Press, Novemeber 2014, ISBN: 9781107077232, On-line http://infolab.stanford.edu/~ullman/mmds/book.pdf [2014-09-12]

Way of continuous check of knowledge in the course of semester

Students' knowledge is verified through the implementation of tasks in exercises, elaboration of data analysis and implementation of some of the discussed methods in the framework of individual work.

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:

Lectures: 1. Big data 2. Sampling methods 3. Dimension Reduction algorithms 4. Aggregation and clustering on big data 5. Advanced algorithm for data classification 6. Ensemble classification algorithms 7. Deep Models, Deep Neural Networks 8. Deep model learning algorithms 9. Recommender systems 10. Data Visualization Excercise: Practical evaluation of the theory on real-world datasets.

Conditions for subject completion

Full-time form (validity from: 2019/2020 Winter semester, validity until: 2022/2023 Summer semester)

Task name	Type of task	Max. number of points (act. for subtasks)	Min. number of points	Max. počet pokusů
Graded credit	Graded credit	100	51	3

Valid from	Valid until	Mandatory attendence participation
Mar 26, 2019 3:59:14 PM	Sep 15, 2022 9:54:03 PM	Completion of the task defined by teacher.

Mandatory attendence participation: Participation in the exercises is compulsory and is monitored. The amount of the compulsory participation will be communicated to the students by the course supervisor at the beginning of the semester.

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Conditions for subject completion and attendance at the exercises within ISP: Completion of all mandatory tasks within individually agreed deadlines.

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Occurrence in study plans

Academic year	Programme	Zaměření	Form	Study language	Tut. centre	Year	Type of duty
2022/2023	(N0688A140015) Industry 4.0	AZD	P	English	Ostrava	2	Compulsory	study plan
2021/2022	(N0688A140015) Industry 4.0	AZD	P	English	Ostrava	2	Compulsory	study plan
2020/2021	(N0688A140015) Industry 4.0	AZD	P	English	Ostrava	2	Compulsory	study plan

Occurrence in special blocks

Block name	Academic year	Form of study	Study language	Year	W	S	Type of block	Block owner

Assessment of instruction

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