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Lecturer(s)
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Holý Richard, Ing. Bc. Ph.D.
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Course content
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The lectures and practicals of the course are focused on the basic knowledge and practical skills in the field of analysis of technological and technical processes and procedures, focusing on the identification of weaknesses and opportunities for improvement. Students will be introduced to modern methods for analyzing and streamlining these processes, including hw used for process control and process data collection. Lecture 1. Introduction to Python 2. Programming and controlling hardware in Python 3. IoT in industrial and engineering processes 4. Sensors and actuators, robotics 5. Data acquisition using Python and IoT 6. Communication and data exchange to support efficient processes 7. Data preparation for process efficiency analysis 8. Working with and evaluating data (Numpy, Pandas, Matplotlib) 9. Statistical evaluation of data in Python 10. Machine learning and efficiency in processes 11. Machine learning for efficient processes in industry and engineering 12. Presentation of concrete projects from practice 13. Presentation of concrete projects from practice
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Learning activities and teaching methods
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Lecture with practical applications, E-learning, One-to-One tutorial, Seminar classes, Individual study
- unspecified
- 15 hours per semester
- Undergraduate study programme term essay (20-40)
- 20 hours per semester
- Preparation for an examination (30-60)
- 26 hours per semester
- Contact hours
- 52 hours per semester
- Preparation for comprehensive test (10-40)
- 11 hours per semester
- Presentation preparation (report) (1-10)
- 6 hours per semester
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| prerequisite |
|---|
| Knowledge |
|---|
| understand basic mathematical logic |
| have knowledge of basic mathematical operations |
| have a basic understanding of algorithmisation |
| Skills |
|---|
| have knowledge of working with PC |
| logical reasoning |
| have the ability to algorithmise |
| Competences |
|---|
| N/A |
| N/A |
| N/A |
| N/A |
| learning outcomes |
|---|
| Knowledge |
|---|
| have a basic understanding of selected software tools and programming languages |
| have a basic understanding of working with data using selected tools |
| have a basic understanding of machine learning |
| Skills |
|---|
| have basic knowledge of selected software tools or programming languages |
| have a basic understanding of the use of machine learning libraries |
| have a basic understanding of machine learning |
| Competences |
|---|
| N/A |
| N/A |
| N/A |
| teaching methods |
|---|
| Knowledge |
|---|
| Lecture |
| Lecture with visual aids |
| Lecture supplemented with a discussion |
| Interactive lecture |
| E-learning |
| Task-based study method |
| Skills demonstration |
| Project-based instruction |
| Cooperative instruction |
| Individual study |
| Students' portfolio |
| Discussion |
| Skills |
|---|
| Practicum |
| E-learning |
| Task-based study method |
| Project-based instruction |
| Cooperative instruction |
| Self-study of literature |
| Individual study |
| Students' portfolio |
| Discussion |
| Competences |
|---|
| Task-based study method |
| Project-based instruction |
| Individual study |
| Students' portfolio |
| Discussion |
| assessment methods |
|---|
| Knowledge |
|---|
| Combined exam |
| Test |
| Individual presentation at a seminar |
| Skills |
|---|
| Combined exam |
| Project |
| Peer evaluation of students |
| Competences |
|---|
| Individual presentation at a seminar |
| Project |
| Peer evaluation of students |
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Recommended literature
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AI PUBLISHING. Python Scikit-Learn For Beginners: Scikit-Learn Specialization For Data Scientist. 2021. ISBN 978-1-956591-0978.
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Géron, Aurélien. Hands-on machine learning with Scikit-Learn and TensorFlow : concepts, tools, and techniques to build intelligent systems. 2nd edition updated for TensorFlow 2. 2019. ISBN 978-1-492-03264-9.
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Mark Pilgrim. Ponořme se do Pythonu 3. Praha, 2017. ISBN 978-80-904248-2-1.
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Matthes, Eric. Python crash course : a hands-on, project-based introduction to programming. 3rd edition. 2023. ISBN 978-1-7185-0270-3.
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Pecinovský, Rudolf. Začínáme programovat v jazyku Python. První vydání. 2020. ISBN 978-80-271-1237-1.
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RASCHKA, Sebastian, Yuxi LIU, Vahid MIRJALILI a Dmytro DZHULGAKOV. Machine learning with PyTorch and Scikit-Learn: develop machine learning and deep learning models with Python.. 2022. ISBN 9781801819312.
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Řepa, Václav. Podnikové procesy - procesní řízení a modelování. GRADA Publishing, 2007. ISBN 978-80-247-2252-8.
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ŘEPA, Václav. Procesně řízená organizace. Praha: Grada Publishing, 2012. ISBN 978-80-247-4128-4.
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SMART, Gary. Practical Python Programming for IoT: Build advanced IoT projects using a Raspberry Pi 4, MQTT, RESTful APIs, WebSockets, and Python 3. Birmingham: Packt Publishing, 2020. ISBN 978-1838982461.
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