VŠTE:SNS Machine Learning and Neural Ne - Course Information
SNS Machine Learning and Neural Network
Institute of Technology and Business in České Budějovicesummer 2024
- Extent and Intensity
- 0/2/0. 2 credit(s). Type of Completion: z (credit).
Taught in person. - Teacher(s)
- Ing. Tereza Matasová (seminar tutor)
- Guaranteed by
- Ing. Tereza Matasová
School of Expertness and Valuation – Rector – Institute of Technology and Business in České Budějovice
Contact Person: Ing. Tereza Matasová
Supplier department: Economics Group – Deputy Director of Department for Research, Development and Creative Activity – School of Expertness and Valuation – Rector – Institute of Technology and Business in České Budějovice - Timetable of Seminar Groups
- SNS/E4: Sun 12. 5. 14:50–16:20 D315, 16:30–18:00 D315, Sun 2. 6. 8:00–9:30 D315, 9:40–11:10 D315, T. Matasová
- Prerequisites
- MAX_PREZENCNICH(35) && MAX_KOMBINOVANYCH(35)
Základy práce se SW Mathematica. - Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Business analyst (programme VŠTE, BSA)
- Business Economics (programme VŠTE, B_PE)
- 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. Porozumění syntaxe Wolfram Mathematica a základní matematické úpravy
- 2. Porozumění syntaxe Wolfram Mathematica a základní matematické úpravy
- 3. Porozumění syntaxe Wolfram Mathematica a základní matematické úpravy
- 4. Porozumění syntaxe Wolfram Mathematica a základní matematické úpravy
- 5. Proces přípravy a získání vědeckých dat
- 6. Import a export
- 7. Regrese
- 8. Predikce
- 9. Predikce
- 10. Klasifikace
- 11. Shluková analýza
- 12. Shluková analýza
- 13. Práce s textem
- Literature
- required literature
- BERNARD, Etienne. Introduction To Machine Learning. Wolfram Media, 2021. ISBN 1-57955-048-7. info
- VILLALOBOS ALVA, Jalil. Beginning Mathematica and Wolfram for Data Science: Applications in Data Analysis, Machine Learning, and Neural Networks. Apress, 2021. ISBN 978-1-4842-6593-2. info
- WOLFRAM, Stephen. An Elementary Introduction to the Wolfram Language. Online. 3. vydání. Wolfram Media, 2023. ISBN 978-1-944183-07-3. URL info
- Forms of Teaching
- Seminar
- Teaching Methods
- Frontal Teaching
Critical Thinking
Individual Work– Individual or Individualized Activity
Teaching Supported by Multimedia Technologies
E-learning
- Student Workload
Activities Number of Hours of Study Workload Daily Study Combined Study Preparation for Seminars, Exercises, Tutorial 5 15 Preparation for the Final Test 3 25 Seminar work 18 Attendance on Seminars/Exercises/Tutorial/Excursion 26 12 Total: 52 52 - Assessment Methods and Assesment Rate
- Project – semestral 100 %
- Language of instruction
- Czech
- Enrolment Statistics (recent)
- Permalink: https://is.vstecb.cz/course/vste/summer2024/SNS