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 | Optional |

Year | 2 | Semester | winter |

Study language | Czech | ||

Year of introduction | 2022/2023 | Year of cancellation | |

Intended for the faculties | FEI, FMT | Intended for study types | Follow-up Master |

Instruction secured by | |||
---|---|---|---|

Login | Name | Tuitor | Teacher giving lectures |

PLA06 | prof. Ing. Jan Platoš, Ph.D. | ||

PRO0199 | Ing. Petr Prokop |

Extent of instruction for forms of study | ||
---|---|---|

Form of study | Way of compl. | Extent |

Full-time | Graded credit | 2+2 |

Part-time | Graded credit | 18+0 |

The course aims to provide students with a detailed overview of procedures and methods in machine learning, from exploratory data analysis, through the search for similarity, comparison of objects to the search for classification models. Students will have the chance to implement and test individual methods on artificial and real data and evaluate the results they will learn to present correctly.

Lectures

Tutorials

In the course, students get acquainted with the properties of data, their storage, and processing. They will also get acquainted with data analysis methods, machine learning, artificial intelligence, interpretation of results, and visualization. Lectures will focus on basic methods of analysis and data and extraction of findings extracted from data. Students will decide for themselves when which method is suitable, its assumptions, what its principle is, and what outputs can be obtained with it. The exercise will then be used for practical experiments on suitable data sets, experimentation with tools for data analysis, and evaluation of results.

- Slides from Lectures
[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.

[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.

The student knowledge is checked during lab using exercises, home works and implementation of selected algorithm.

Additional requirements are not placed on the student.

Subject has no prerequisities.

Subject has no co-requisities.

Lectures (topics):
1. Frequent patterns in data.
2. Exploratory data analysis.
3. Representative clustering, Hierarchical clustering.
4. Clustering based on data density, cluster validation.
5. Special clustering methods, detection of outliers.
6. Linear classifiers (Linear discriminant analysis, Naive Bayes, Logistic regression).
7. Decision trees, rule classification.
8. Support Vector Machine, Kernel methods.
9. Neural networks.
10. Regression methods and Advanced methods in data classification.
11. Validation of classification algorithms.
12. Time series analysis.
Exercises in the computer room (topics):
1. Implementation of the APRIORI method for searching for rules in data.
2. Exploratory analysis of data over a real dataset using appropriate tools.
3. Implementation of hierarchical clustering - Agglomerative clustering.
4. Implementation of DBSCAN algorithm.
5. Real example of clustering - independent work on exercises.
6. Dimension reduction.
7. Implementation of decision tree.
8. Testing the Support Vector Machine method over real data.
9. Neural networks.
10. Regression methods.
11. Ensemble methods and their use.
12. Classification - real example.
13. Time series analysis.

Task name | Type of task | Max. number of points
(act. for subtasks) | Min. number of points | Max. počet pokusů |
---|---|---|---|---|

Graded credit | Graded credit | 100 (100) | 51 | 3 |

Explorativní analýza dat | Project | 40 | 20 | |

Shlukování dat | Project | 30 | 15 | |

Klasifikace dat | Project | 30 | 15 |

Show history

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

Show history

Academic year | Programme | Branch/spec. | Spec. | Zaměření | Form | Study language | Tut. centre | Year | W | S | Type of duty | |
---|---|---|---|---|---|---|---|---|---|---|---|---|

2024/2025 | (N0612A140004) Information and Communication Security | IKB | P | Czech | Ostrava | 1 | Compulsory | study plan | ||||

2024/2025 | (N0613A140034) Computer Science | DS | P | Czech | Ostrava | 1 | Choice-compulsory type A | study plan | ||||

2024/2025 | (N0613A140034) Computer Science | AZD | P | Czech | Ostrava | 1 | Choice-compulsory type A | study plan | ||||

2024/2025 | (N0613A140034) Computer Science | DS | K | Czech | Ostrava | 1 | Choice-compulsory type A | study plan | ||||

2024/2025 | (N0613A140034) Computer Science | AZD | K | Czech | Ostrava | 1 | Choice-compulsory type A | study plan | ||||

2024/2025 | (N0688A140014) Industry 4.0 | AZD | P | Czech | Ostrava | 1 | Compulsory | study plan | ||||

2024/2025 | (N0541A170007) Computational and Applied Mathematics | (S01) Applied Mathematics | K | Czech | Ostrava | 2 | Optional | study plan | ||||

2024/2025 | (N0541A170007) Computational and Applied Mathematics | (S01) Applied Mathematics | P | Czech | Ostrava | 2 | Optional | study plan | ||||

2024/2025 | (N0541A170007) Computational and Applied Mathematics | (S02) Computational Methods and HPC | P | Czech | Ostrava | 2 | Optional | study plan | ||||

2024/2025 | (N0541A170007) Computational and Applied Mathematics | (S02) Computational Methods and HPC | K | Czech | Ostrava | 2 | Optional | study plan | ||||

2024/2025 | (N0613A140034) Computer Science | SWI | P | Czech | Ostrava | 1 | Choice-compulsory type B | study plan | ||||

2024/2025 | (N0613A140034) Computer Science | SWI | K | Czech | Ostrava | 1 | Choice-compulsory type B | study plan | ||||

2023/2024 | (N0612A140004) Information and Communication Security | IKB | P | Czech | Ostrava | 1 | Compulsory | study plan | ||||

2023/2024 | (N0688A140014) Industry 4.0 | AZD | P | Czech | Ostrava | 1 | Compulsory | study plan | ||||

2023/2024 | (N0613A140034) Computer Science | DS | K | Czech | Ostrava | 1 | Choice-compulsory type A | study plan | ||||

2023/2024 | (N0613A140034) Computer Science | AZD | K | Czech | Ostrava | 1 | Choice-compulsory type A | study plan | ||||

2023/2024 | (N0613A140034) Computer Science | DS | P | Czech | Ostrava | 1 | Choice-compulsory type A | study plan | ||||

2023/2024 | (N0613A140034) Computer Science | AZD | P | Czech | Ostrava | 1 | Choice-compulsory type A | study plan | ||||

2023/2024 | (N0541A170007) Computational and Applied Mathematics | (S01) Applied Mathematics | P | Czech | Ostrava | 2 | Optional | study plan | ||||

2023/2024 | (N0541A170007) Computational and Applied Mathematics | (S01) Applied Mathematics | K | Czech | Ostrava | 2 | Optional | study plan | ||||

2023/2024 | (N0541A170007) Computational and Applied Mathematics | (S02) Computational Methods and HPC | P | Czech | Ostrava | 2 | Optional | study plan | ||||

2023/2024 | (N0541A170007) Computational and Applied Mathematics | (S02) Computational Methods and HPC | K | Czech | Ostrava | 2 | Optional | study plan | ||||

2023/2024 | (N2647) Information and Communication Technology | (2612T025) Computer Science and Technology | P | Czech | Ostrava | 2 | Choice-compulsory | study plan | ||||

2023/2024 | (N2647) Information and Communication Technology | (2612T025) Computer Science and Technology | K | Czech | Ostrava | 2 | Choice-compulsory | study plan | ||||

2022/2023 | (N0613A140034) Computer Science | DS | K | Czech | Ostrava | 1 | Choice-compulsory type A | study plan | ||||

2022/2023 | (N0613A140034) Computer Science | AZD | K | Czech | Ostrava | 1 | Choice-compulsory type A | study plan | ||||

2022/2023 | (N0613A140034) Computer Science | DS | P | Czech | Ostrava | 1 | Choice-compulsory type A | study plan | ||||

2022/2023 | (N0613A140034) Computer Science | AZD | P | Czech | Ostrava | 1 | Choice-compulsory type A | study plan | ||||

2022/2023 | (N0688A140014) Industry 4.0 | AZD | P | Czech | Ostrava | 1 | Compulsory | study plan | ||||

2022/2023 | (N0612A140004) Information and Communication Security | IKB | P | Czech | Ostrava | 1 | Compulsory | study plan | ||||

2022/2023 | (N0541A170007) Computational and Applied Mathematics | (S01) Applied Mathematics | K | Czech | Ostrava | 2 | Optional | study plan | ||||

2022/2023 | (N0541A170007) Computational and Applied Mathematics | (S01) Applied Mathematics | P | Czech | Ostrava | 2 | Optional | study plan | ||||

2022/2023 | (N0541A170007) Computational and Applied Mathematics | (S02) Computational Methods and HPC | K | Czech | Ostrava | 2 | Optional | study plan | ||||

2022/2023 | (N0541A170007) Computational and Applied Mathematics | (S02) Computational Methods and HPC | P | Czech | Ostrava | 2 | Optional | study plan | ||||

2022/2023 | (N2647) Information and Communication Technology | (2612T025) Computer Science and Technology | P | Czech | Ostrava | 2 | Choice-compulsory | study plan | ||||

2022/2023 | (N2647) Information and Communication Technology | (2612T025) Computer Science and Technology | K | Czech | Ostrava | 2 | Choice-compulsory | study plan |

Block name | Academic year | Form of study | Study language | Year | W | S | Type of block | Block owner |
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2023/2024 Winter |

2022/2023 Winter |