| Course Unit Code | 460-4162/01 |
|---|
| Number of ECTS Credits Allocated | 5 ECTS credits |
|---|
| Type of Course Unit * | Optional |
|---|
| Level of Course Unit * | Second Cycle |
|---|
| Year of Study * | |
|---|
| Semester when the Course Unit is delivered | Winter Semester |
|---|
| Mode of Delivery | Face-to-face |
|---|
| Language of Instruction | English |
|---|
| Prerequisites and Co-Requisites | Course succeeds to compulsory courses of previous semester |
|---|
| Name of Lecturer(s) | Personal ID | Name |
|---|
| PLA06 | prof. Ing. Jan Platoš, Ph.D. |
| Summary |
|---|
| The course introduces students to the characteristics of data, its storage, and processing options. Emphasis is placed on data analysis methods, classical machine learning techniques, and modern neural networks, including convolutional and recurrent architectures and autoencoders. Students will learn to interpret and visualize the results obtained and understand when it is appropriate to use individual methods, as well as their principles, assumptions, and expected outputs. Lectures will focus on methodology and principles, while exercises will provide space for practical experiments with real data sets, working with analysis tools, and critical evaluation of the results obtained. |
| Learning Outcomes of the Course Unit |
|---|
The course aims to provide students with a comprehensive overview of machine learning methods and procedures, guiding them towards their practical application. Students will learn to perform exploratory data analysis, search for similarities and compare objects, and create and evaluate classification models. They will master classical machine learning methods, including linear and logistic regression, decision trees, and clustering methods. They will also gain knowledge of the principles of neural networks, including convolutional and recurrent architectures, and learn the basics of autoencoders.
Skills:
- independently implement and apply selected machine learning methods,
- prepare and process data sets for analytical tasks,
- select an appropriate model based on the nature of the problem and data,
- evaluate accuracy and interpret results,
- present analytical procedures and outputs in a professional environment.
Competencies:
- ability to solve complex tasks in the field of machine learning using both classical and modern methods,
- orientation in the possibilities and limitations of individual approaches,
- readiness to collaborate in a team on the design and implementation of data-oriented solutions,
- ability to critically assess the quality of a model and its practical benefits.
|
| Course Contents |
|---|
The main topics covered in the course are:
- Clustering methods and their validation.
- Classification methods and their validation.
- Regression methods and their validation.
- Kernel methods and Support Vector Machines.
- Neural networks, including convolutional and recurrent networks.
- Autoencoders and Variational Autoencoders.
- Signal and time series analysis.
During the exercises, students will test their knowledge using both real and artificial data and apply basic principles. |
| Recommended or Required Reading |
|---|
| Required Reading: |
|---|
- Lecture Slides
[1] AGGARWAL, Charu C. Data mining: the textbook. New York, NY: Springer Science+Business Media, 2015. ISBN 978-3-319-14141-1.
[2] BRAMER, M. A. Principles of data mining. London: Springer, 2007. ISBN 1-84628-765-0.
|
- Prezentace k přednáškám.
[1] AGGARWAL, Charu C. Data mining: the textbook. New York, NY: Springer Science+Business Media, 2015. ISBN 978-3-319-14141-1.
[2] BRAMER, M. A. Principles of data mining. London: Springer, 2007. ISBN 1-84628-765-0.
|
| Recommended Reading: |
|---|
[1] LESKOVEC, Jure, Anand RAJARAMAN a Jeffrey D. ULLMAN. Mining of massive datasets, Standford University. Second edition. Cambridge: Cambridge University Press, 2014. ISBN 9781107077232.
[2] 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.
[3] ZAKI, Mohammed J. a Wagner MEIRA JR. Data Mining and Analysis: Fundamental Concepts and Algorithms. 2nd edition. Cambridge, GB: Cambridge University Press, 2020. ISBN 978-0521766333. |
[1] LESKOVEC, Jure, Anand RAJARAMAN a Jeffrey D. ULLMAN. Mining of massive datasets, Standford University. Second edition. Cambridge: Cambridge University Press, 2014. ISBN 9781107077232.
[2] 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.
[3] ZAKI, Mohammed J. a Wagner MEIRA JR. Data Mining and Analysis: Fundamental Concepts and Algorithms. 2nd edition. Cambridge, GB: Cambridge University Press, 2020. ISBN 978-0521766333. |
| Planned learning activities and teaching methods |
|---|
| Lectures, Tutorials, Teaching by an expert (lecture or tutorial) |
| Assesment methods and criteria |
|---|
| Tasks are not Defined |