Artificial Intelligence for UAS
ปัญญาประดิษฐ์สำหรับระบบอากาศยานไร้คนขับ
Course description
artificial intelligence fundamentals, machine learning, data preparation, classification, regression, neural networks, deep learning, model evaluation, use of sensor and image data, and deployment of models in unmanned aircraft systems
Thai description
พื้นฐานปัญญาประดิษฐ์ การเรียนรู้ของเครื่อง การเตรียมข้อมูล การจำแนกประเภท การถดถอย โครงข่ายประสาทเทียม การเรียนรู้เชิงลึก การประเมินแบบจำลอง การใช้ข้อมูลจากเซนเซอร์และภาพ และการนำแบบจำลองไปใช้กับระบบอากาศยานไร้คนขับ
Course learning outcomes (CLO)
| CLO | Outcome | PLO |
|---|---|---|
| CLO1 | Explain machine learning and deep learning principles | PLO4 |
| CLO2 | Prepare drone image datasets correctly | PLO4PLO6 |
| CLO3 | Train, evaluate and deploy models | PLO4 |
Learning modules
1AI in drone work
Weeks 1–3 · 27 hLesson 85 minquiz2 KU
Online (before class) · 15 h
Study the assigned knowledge units in advance, review media and take the module quiz
In class / field · 12 h
Lab or field practice from worksheets with a safety checklist
2Machine learning fundamentals
Weeks 4–6 · 27 hLesson 90 minquiz1 KU
Online (before class) · 15 h
Study the assigned knowledge units in advance, review media and take the module quiz
- Machine learning fundamentalsIn development
In class / field · 12 h
Lab or field practice from worksheets with a safety checklist
3Datasets and labelling
Weeks 7–9 · 27 hLesson 85 minquiz3 KU
Online (before class) · 15 h
Study the assigned knowledge units in advance, review media and take the module quiz
In class / field · 12 h
Lab or field practice from worksheets with a safety checklist
4Deep learning and object detection
Weeks 10–12 · 27 hLesson 90 minquiz3 KU
Online (before class) · 15 h
Study the assigned knowledge units in advance, review media and take the module quiz
In class / field · 12 h
Lab or field practice from worksheets with a safety checklist
5Model evaluation and edge
Weeks 13–15 · 27 hLesson 85 minquiz2 KU
Online (before class) · 15 h
Study the assigned knowledge units in advance, review media and take the module quiz
In class / field · 12 h
Lab or field practice from worksheets with a safety checklist
Assessment (draft)
| Labs and worksheets | 35% |
| Module quizzes | 10% |
| Midterm examination | 20% |
| Mini-project or practical exam | 35% |
Knowledge domain
Key references
- Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow (3rd ed.). O'Reilly.
- Prince, S. J. D. (2023). Understanding deep learning. MIT Press. link