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Fundamentals of Machine Learning

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Course Unit Code460-2064/01
Number of ECTS Credits Allocated4 ECTS credits
Type of Course Unit *Choice-compulsory type B
Level of Course Unit *First Cycle
Year of Study *Third Year
Semester when the Course Unit is deliveredWinter Semester
Mode of DeliveryFace-to-face
Language of InstructionCzech
Prerequisites and Co-Requisites Course succeeds to compulsory courses of previous semester
Name of Lecturer(s)Personal IDName
PLA06prof. Ing. Jan Platoš, Ph.D.
Summary
The students will learn about the data properties, data storage, and processing in the course. They will also learn methods of data analysis, machine learning, artificial intelligence, interpretation of results and their visualization. The lectures will deal with statistical properties of data, methods of data cleaning and preprocessing. Next, the theoretical description of methods of data processing, machine learning, and artificial intelligence. Students will be able to decide which method is appropriate, what assumptions, what is its principle, and what outputs it can get. The exercises will then serve for practical experiments on suitable datasets, experimenting with data analysis tools, and evaluating results.
Learning Outcomes of the Course Unit
The course aims to acquaint students with the data, data analysis, and machine learning at a level that will match absolved subjects and their level of knowledge. The primary knowledge that will be given to students is knowledge about data, their preparation, statistical properties, data processing methods and machine learning. Students will be able to understand the data properties, analytical methods, and will be able to correctly interpret the results achieved and present and visualize these methods.
Course Contents
Lectures:
1. Data and their Properties
2. Statistical Data Features
3. Knowledge Representation
4. Basic Algorithms
5. Credibility and Algorithm evaluation
6. Advanced Methods and Algorithms
7. Extending of Linear Model
8. Data Transformation
9. Optimization methods
10. Data Vizualization

Exercises on computer lab:
1. Demonstration of lecture knowledge - data and the properties.
2. Demonstration of lecture knowledge - statistical data proeprties.
3. Demonstration of lecture knowledge - knowledge representations.
4. Demonstration of lecture knowledge - linear models.
5. Demonstration of lecture knowledge - model quality and its measurement.
6. Demonstration of lecture knowledge - non=linear models.
7. Demonstration of lecture knowledge - data transformation.
8. Demonstration of lecture knowledge - data transformation.
9. Demonstration of lecture knowledge - optimization method introduction.
10. Demonstration of lecture knowledge - data visualization.
Recommended or Required Reading
Required Reading:
Presentation for lectures.
HASTIE, Trevor., Robert. TIBSHIRANI and J. H. FRIEDMAN. The elements of statistical learning: data mining, inference, and prediction. 2nd ed. New York, NY: Springer, c2009. ISBN 978-0-387-84858-7.
WITTEN, Ian H., Eibe FRANK, Mark A. HALL and Christopher J. PAL. Data mining: Practical machine learning tools and techniques. Fourth Edition. Amsterdam: Elsevier, 2017. ISBN 978-0-12-804291-5.
Prezentace k přednáškám
HASTIE, Trevor., Robert. TIBSHIRANI a J. H. FRIEDMAN. The elements of statistical learning: data mining, inference, and prediction. 2nd ed. New York, NY: Springer, c2009. ISBN 978-0-387-84858-7.
WITTEN, Ian H., Eibe FRANK, Mark A. HALL a Christopher J. PAL. Data mining: Practical machine learning tools and techniques. Fourth Edition. Amsterdam: Elsevier, 2017. ISBN 978-0-12-804291-5.

Recommended Reading:
LESKOVEC, Jurij, Anand RAJARAMAN and Jeffrey D. ULLMAN. Mining of massive datasets / Jure Leskovec, Standford University, Anand Rajaraman, Milliways Labs, Jeffrey David Ullman, Standford University. Second edition. Cambridge: Cambridge University Press, 2014. ISBN 9781107077232.
AGGARWAL, Charu C. Data mining: the textbook. New York, NY: Springer Science+Business Media, 2015. ISBN 978-3-319-14141-1.
LESKOVEC, Jurij, Anand RAJARAMAN a Jeffrey D. ULLMAN. Mining of massive datasets / Jure Leskovec, Standford University, Anand Rajaraman, Milliways Labs, Jeffrey David Ullman, Standford University. Second edition. Cambridge: Cambridge University Press, 2014. ISBN 9781107077232.
AGGARWAL, Charu C. Data mining: the textbook. New York, NY: Springer Science+Business Media, 2015. ISBN 978-3-319-14141-1.
Planned learning activities and teaching methods
Lectures, Tutorials
Assesment methods and criteria
Task TitleTask TypeMaximum Number of Points
(Act. for Subtasks)
Minimum Number of Points for Task Passing
Graded creditGraded credit100 (100)51
        Realizace úloh na cvičeníOther task type20 10
        Exploratory analysisProject30 15
        Shlukování datProject25 12
        Klasifikace datProject25 12