Artificial Intelligence for UAS
ปัญญาประดิษฐ์สำหรับระบบอากาศยานไร้คนขับ
Course description
Artificial intelligence and machine-learning applications for UAS data, imagery and sensors; classification, detection, prediction, data analysis, and autonomous decision-making.
Thai description
AI และ Machine Learning สำหรับข้อมูล ภาพ และ Sensor จาก UAS การจำแนก ตรวจจับวัตถุ การพยากรณ์ การวิเคราะห์ข้อมูล และ Autonomous Decision-Making
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 · 30 h2 KU
Online (before class) · 15 h
Study the assigned knowledge units in advance, review media and take the module quiz
In class / field · 15 h
Lab or field practice from worksheets with a safety checklist
2Machine learning fundamentals
Weeks 4–6 · 30 h1 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 · 15 h
Lab or field practice from worksheets with a safety checklist
3Datasets and labelling
Weeks 7–9 · 30 h3 KU
Online (before class) · 15 h
Study the assigned knowledge units in advance, review media and take the module quiz
In class / field · 15 h
Lab or field practice from worksheets with a safety checklist
4Deep learning and object detection
Weeks 10–12 · 30 h3 KU
Online (before class) · 15 h
Study the assigned knowledge units in advance, review media and take the module quiz
In class / field · 15 h
Lab or field practice from worksheets with a safety checklist
5Model evaluation and edge
Weeks 13–15 · 30 h2 KU
Online (before class) · 15 h
Study the assigned knowledge units in advance, review media and take the module quiz
In class / field · 15 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