VŠTE:SNS Machine Learning and Neural Ne - Course Information
SNS Machine Learning and Neural Network
Institute of Technology and Business in České Budějovicesummer 2020
- Extent and Intensity
- 0/2/0. 2 credit(s). Type of Completion: z (credit).
- Guaranteed by
- doc. Ing. Vojtěch Stehel, MBA, PhD.
Faculty of Technology – Rector – Institute of Technology and Business in České Budějovice
Supplier department: Faculty of Technology – Rector – Institute of Technology and Business in České Budějovice - Prerequisites
- FORMA(P)
Basics of working with Matlab, or willingness to learn them in the first weeks. - Course Enrolment Limitations
- The course is offered to students of any study field.
- Course objectives supported by learning outcomes
- Students will learn the most common algorithms for machine learning. He is able to optimize these algorithms and apply them in practice.
- Learning outcomes
- The student knows commonly used algorithms for machine learning including basic neural networks. Students can practically use algorithms in their application. The student can also optimize the results. The student is able to understand the principle of machine learning, errors that can occur in coding and interpretation of results.
- Syllabus
- 1. Machine learning - introduction 2. Data acquisition and preparation 3. Regression and classification 4. Nearest Neighbor 5. Naive Bayes Classification 6. Discriminant Analysis 7. Support Vector Machines 8. Trees 9. Gaussian Process Regression 10. Improving Predictive Models 11. Neural network 12. Self-Organizing Maps and Feed-Forward Networks 13. Deep Learning
- Literature
- recommended literature
- Kvasnička, V. - Beňušková, L. - Pospíchal, J. - Farkaš, I. - Tiňo, P. - Kráľ, A.:Úvod do teórie neurónových sietí. IRIS, Bratislava 1997.
- Šíma, J. Generalized back propagation for interval training patterns, Neural Network World 2 (1992), 167-173.
- Šíma, J. - Neruda, J.: Teoretické otázky neuronových sítí. Matfyzpress, Praha 1996.
- Forms of Teaching
- Seminar
Exercise
Consultation - Teaching Methods
- Frontal Teaching
Individual Work– Individual or Individualized Activity
- Student Workload
Activities Number of Hours of Study Workload Daily Study Combined Study Preparation for Seminars, Exercises, Tutorial 13 Seminární práce (in Czech) 26 Attendance on Seminars/Exercises/Tutorial/Excursion 13 Total: 52 0 - Assessment Methods and Assesment Rate
- Seminary Work 100 %
- Exam conditions
- 100 % seminar work
- Language of instruction
- Czech
- Enrolment Statistics (summer 2020, recent)
- Permalink: https://is.vstecb.cz/course/vste/summer2020/SNS