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Lecturer(s)
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Karban Pavel, prof. Ing. Ph.D.
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Course content
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1. Embedded Workflow and Build Systems 2. CI/CD Pipelines and Static Code Analysis 3. HAL Architecture and Hardware Abstraction 4. Hardware-in-the-Loop (HIL) Testing 5. Emulation and Testing 6. Digital Twins for Development 7. Efficient Data Collection and Processing in MCUs 8. Profiling and Remote System Diagnostics 9. Secure OTA Update Architecture 10. Firmware Security 11. Edge AI and TinyML Model Optimization 12. AI Lifecycle and Deployment Workflow
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Learning activities and teaching methods
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One-to-One tutorial, Group discussion, Individual study
- Preparation for an examination (30-60)
- 30 hours per semester
- Preparation for formative assessments (2-20)
- 12 hours per semester
- Individual project (40)
- 30 hours per semester
- unspecified
- 5 hours per semester
- Contact hours
- 52 hours per semester
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| prerequisite |
|---|
| Knowledge |
|---|
| to know basic principles of structured programming |
| to know basic principles of object-oriented programming |
| to define a list of basic data types and control structures |
| to describe basic data structures and algorithms |
| Skills |
|---|
| to control commonly available information and communication technique |
| to use basic algorithms, data and control structures |
| to create simple computer programs in a structured and object-oriented language |
| Competences |
|---|
| N/A |
| N/A |
| N/A |
| learning outcomes |
|---|
| Knowledge |
|---|
| to design, implement and debug applications using basic and advanced programming principles |
| to analyze and test code to ensure software quality |
| to manage source code using a versioning system |
| Skills |
|---|
| to create and debug functional code based on the given specification |
| to collaborate effectively on software development using modern development tools and techniques |
| Competences |
|---|
| N/A |
| N/A |
| N/A |
| N/A |
| teaching methods |
|---|
| Knowledge |
|---|
| Lecture supplemented with a discussion |
| Lecture |
| Multimedia supported teaching |
| Skills |
|---|
| Practicum |
| Task-based study method |
| Skills demonstration |
| Individual study |
| Competences |
|---|
| Practicum |
| Task-based study method |
| Discussion |
| assessment methods |
|---|
| Knowledge |
|---|
| Combined exam |
| Test |
| Skills |
|---|
| Combined exam |
| Skills demonstration during practicum |
| Seminar work |
| Competences |
|---|
| Combined exam |
| Continuous assessment |
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Recommended literature
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Deep learning v jazyku Python : Knihovny Keras, TensorFlow.
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Hunt, John. A beginners guide to Python 3 programming. 2020. ISBN 978-3-030-20289-7.
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Linge, Svein; Langtangen, Hans Petter. Programming for computations - Python : a gentle introduction to numerical simulations with Python 3.6. Second edition. 2020. ISBN 978-3-030-16876-6.
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Pecinovský, Rudolf. Python : kompletní příručka jazyka pro verzi 3.9. První vydání. 2020. ISBN 978-80-271-1269-2.
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Pecinovský, Rudolf. Začínáme programovat v jazyku Python. První vydání. 2020. ISBN 978-80-271-1237-1.
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