460-4126/02 – Methods of Vector Data Analysis (MAVD)
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 | Compulsory |
Year | 1 | Semester | winter |
| | Study language | English |
Year of introduction | 2019/2020 | Year of cancellation | 2021/2022 |
Intended for the faculties | FEI | Intended for study types | Follow-up Master |
Subject aims expressed by acquired skills and competences
The goal of this course is to deepen and improve the knowledge about data analysis methods acquired in the previous courses. The main information delivered to the students is advanced algorithms for data classification, stream data processing, advanced data structures and machine learning techniques. The students will be able to use these methods, to interpret achieved results. Moreover, the student will be able to presents and visualize the results using proper methods.
Teaching methods
Lectures
Tutorials
Summary
This course is focused on algorithms for data analysis and data visualization. The first part of the course is focused on explorative analysis and data clustering. The second part is focused on the data classification. he course describes a less complex linear methods to the more complex method based on the SVM. More advanced methods will be described in the last part of the course.
Compulsory literature:
Recommended literature:
Way of continuous check of knowledge in the course of semester
Students' knowledge is verified through the implementation of tasks in exercises, elaboration of data analysis and implementation of some of the discussed methods in the framework of individual work.
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:
Exploration data analysis
1. Frequent patterns, Rule based analysis.
2. Representative based clustering, Hierarchical Clustering.
3. Density based clustering, Cluster validation.
4. Self-organizing maps
5. Anomaly detection
Data Classification
6. Linear classification (Linear discriminant analysis, Naive Bayes, Logistics regression)
7. Decision Trees, Random Forests.
8. Support Vector Machine, Kernel based methods
9. Neural networks (Perceptron, Feed forward NN+Back propagation)
10. Regression methods
11. Advanced classification methods
12. Classification validation
Advanced methods
13. Stream dat analysis
14. Vektor data vizualization
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
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