460-6026/02 – Machine Learning (SU)
| Gurantor department | Department of Computer Science | Credits | 10 |
| Subject guarantor | prof. Ing. Jan Platoš, Ph.D. | Subject version guarantor | prof. Ing. Jan Platoš, Ph.D. |
| Study level | postgraduate | Requirement | Choice-compulsory type B |
| Year | | Semester | winter + summer |
| | Study language | English |
| Year of introduction | 2019/2020 | Year of cancellation | |
| Intended for the faculties | FEI | Intended for study types | Doctoral |
Subject aims expressed by acquired skills and competences
The aim of the course is to provide students a detail overview of methodology and methods of machine learning. In addition, this knowledge and skills will be further enhanced in a direction that is in line with the specific focus of its Ph.D. studies and dissertation work.
Teaching methods
Seminars
Individual consultations
Project work
Other activities
Summary
The subject is focused on the data processing and data analysis with respect to knowledge mining. The subject covers all states of data processing from its acquisition, preprocessing and cleaning to classification or clustering and visualization. The main focus will be targeted to the processing of medical and laboratory data but other data sources will be discussed as well.
Compulsory literature:
Recommended literature:
Additional study materials
Way of continuous check of knowledge in the course of semester
Continuous monitoring of study activities and assigned tasks during regular consultations. If some publication activity will be a part of the student's tasks, the relevant article would be presented in the course.
Oral exam.
E-learning
Other requirements
The student prepares and presents the work on a given topic.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
• Data – vector, stream, signals, text, networks.
• Data cleaning, dealing with missing values, aggregation.
• Dimension reduction, dimension expansion.
• Explorative data analysis
• Unsupervised learning – frequent pattern mining, clustering, clustering validation
• Anomaly detection
• Supervised learning
- Classification using linear models
- Classification using probabilistic models
- Classification using non-linear models
- Regression models
• Network data analysis
- Network models
- Clustering, relations
- Community detection
• Data visualization
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
Předmět neobsahuje žádné hodnocení.