SNS Machine Learning and Neural Network

Institute of Technology and Business in České Budějovice
summer 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
ActivitiesNumber of Hours of Study Workload
Daily StudyCombined Study
Preparation for Seminars, Exercises, Tutorial13 
Seminární práce (in Czech)26 
Attendance on Seminars/Exercises/Tutorial/Excursion13 
Total:520
Assessment Methods and Assesment Rate
Seminary Work 100 %
Exam conditions
100 % seminar work
Language of instruction
Czech
The course is also listed under the following terms winter 2020, summer 2021, summer 2024.
  • Enrolment Statistics (summer 2020, recent)
  • Permalink: https://is.vstecb.cz/course/vste/summer2020/SNS