| Course title | Machine Learning a Data Mining 1 |
|---|---|
| Course code | KMI/MLDM1 |
| Organizational form of instruction | Lecture + Exercise |
| Level of course | Master |
| Year of study | not specified |
| Semester | Summer |
| Number of ECTS credits | 4 |
| Language of instruction | Czech |
| Status of course | Compulsory, Compulsory-optional |
| Form of instruction | Face-to-face |
| Work placements | This is not an internship |
| Recommended optional programme components | None |
| Lecturer(s) |
|---|
|
| Course content |
|
1. Intro: Data mining: extracting information from data, KDD, typical tasks. Machine learning: learning from information from data, phases and types. 2. Data: Types of data and attributes, quality a preprocessing (sampling, normalization, discretization), similarity and dissimimlarity of objects, summary statistics a visualization. 3. Classification: Decision trees, overfitting problem, evaluation of performance, rule-based, nearest neighbor, naive Bayes, support vector machines (SVM), regression. 4. Association analysis: Itemsets, rules, Apriori algorithm, interestingness evaluation. 5. Cluster analysis: types of clusters, K-means, hierarchical, density-based, expectation-maximization (EM), quality evaluation.
|
| Learning activities and teaching methods |
| unspecified |
| Learning outcomes |
|
The course is the first part of the two semester course devoted to principles and main methods of data mining and machine learning. After problem introduction with defining these notions and looking at data and their preprocessing the basic data minig methods of classification, association analysis and clustering used (not only) in machine learning are discussed, from the algorithmic point of view.
|
| Prerequisites |
|
unspecified
|
| Assessment methods and criteria |
|
unspecified
|
| Recommended literature |
|
| Study plans that include the course |
| Faculty | Study plan (Version) | Category of Branch/Specialization | Recommended semester | |
|---|---|---|---|---|
| Faculty: Faculty of Science | Study plan (Version): Applied Mathematics (2023) | Category: Mathematics courses | 2 | Recommended year of study:2, Recommended semester: Summer |
| Faculty: Faculty of Science | Study plan (Version): Computer Science - Specialization in Artificial Intelligence (2020) | Category: Informatics courses | 1 | Recommended year of study:1, Recommended semester: Summer |
| Faculty: Faculty of Science | Study plan (Version): Applied Computer Science - Specialization in Software Development (2024) | Category: Informatics courses | 1 | Recommended year of study:1, Recommended semester: Summer |
| Faculty: Faculty of Science | Study plan (Version): Bioinformatics (2021) | Category: Informatics courses | 1 | Recommended year of study:1, Recommended semester: Summer |
| Faculty: Faculty of Science | Study plan (Version): Computer Science - Specialization in General Computer Science (2020) | Category: Informatics courses | 1 | Recommended year of study:1, Recommended semester: Summer |
| Faculty: Faculty of Science | Study plan (Version): Applied Computer Science - Specialization in Computer Systems and Technologies (2024) | Category: Informatics courses | 1 | Recommended year of study:1, Recommended semester: Summer |