460-2064/01 – Fundamentals of Machine Learning (ZSU)
| 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 | Choice-compulsory type B |
| Year | 3 | Semester | winter |
| | Study language | Czech |
| Year of introduction | 2019/2020 | Year of cancellation | |
| Intended for the faculties | FEI | Intended for study types | Bachelor |
Subject aims expressed by acquired skills and competences
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.
Teaching methods
Lectures
Tutorials
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.
Compulsory literature:
Recommended literature:
Way of continuous check of knowledge in the course of semester
The student knowledge is checked during lab using exercises, home works and implementation of selected algorithm.
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. 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.
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction