SNS Machine Learning and Neural Network

Institute of Technology and Business in České Budějovice
summer 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
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
ActivitiesNumber of Hours of Study Workload
Daily StudyCombined Study
Preparation for Seminars, Exercises, Tutorial515
Preparation for the Final Test325
Seminar work18 
Attendance on Seminars/Exercises/Tutorial/Excursion2612
Total:5252
Assessment Methods and Assesment Rate
Project – semestral 100 %
Language of instruction
Czech
The course is also listed under the following terms summer 2020, winter 2020, summer 2021.
  • Enrolment Statistics (recent)
  • Permalink: https://is.vstecb.cz/course/vste/summer2024/SNS