UAT 482 · 3(2-2-5) · Year/term 4/1

Machine Learning Technology and Applications

เทคโนโลยีการเรียนรู้ของเครื่องและการประยุกต์

Progress

Notional hours: 135 h (online/self-study 75 · in class/lab 60)

Course description

machine-learning development workflows, data preparation and partitioning, supervised and unsupervised learning, classification, regression, clustering, dimensionality reduction, performance evaluation, model tuning, and applications to automation and UAS data

Thai description

กระบวนการพัฒนาแบบจำลองการเรียนรู้ของเครื่อง การเตรียมและแบ่งข้อมูล การเรียนรู้แบบมีผู้สอนและไม่มีผู้สอน การจำแนก การถดถอย การจัดกลุ่ม การลดมิติ การประเมินประสิทธิภาพ การปรับแบบจำลอง และการประยุกต์กับข้อมูลจากระบบอัตโนมัติและอากาศยานไร้คนขับ

Description source: Curriculum draft (revised 28 Sep 2026)Previous site code: DRT 491

Course learning outcomes (CLO)

CLO	Outcome	PLO
CLO1	Explain core machine learning algorithms	PLO4
CLO2	Develop and evaluate models with real data	PLO4
CLO3	Apply ML to drone data ethically	PLO4PLO6

Learning modules

1ML fundamentals
Weeks 1–3 · 27 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 · 12 h

Lab or field practice from worksheets with a safety checklist

Learning evidence: Checked worksheets and quiz results

Open module

2Supervised learning
Weeks 4–6 · 27 h2 KU

Online (before class) · 15 h

Study the assigned knowledge units in advance, review media and take the module quiz

Machine learning fundamentalsIn development
PyTorchTH

In class / field · 12 h

Lab or field practice from worksheets with a safety checklist

Learning evidence: Checked worksheets and quiz results

Open module

3Data splitting and leakage
Weeks 7–9 · 27 h1 KU

Online (before class) · 15 h

Study the assigned knowledge units in advance, review media and take the module quiz

Checking datasets before AI trainingTH

In class / field · 12 h

Lab or field practice from worksheets with a safety checklist

Learning evidence: Checked worksheets and quiz results

Open module

4Deep learning
Weeks 10–12 · 27 h2 KU

Online (before class) · 15 h

Study the assigned knowledge units in advance, review media and take the module quiz

Deep learning and CNNsIn development
Google ColabTH

In class / field · 12 h

Lab or field practice from worksheets with a safety checklist

Learning evidence: Checked worksheets and quiz results

Open module

5Evaluation and ethics
Weeks 13–15 · 27 h2 KU

Online (before class) · 15 h

Study the assigned knowledge units in advance, review media and take the module quiz

Checking image quality and reading AI metricsTH
Ethics, privacy and the PDPA in drone workIn development

In class / field · 12 h

Lab or field practice from worksheets with a safety checklist

Learning evidence: Checked worksheets and quiz results

Open module

Assessment (draft)

Labs and worksheets	35%
Module quizzes	10%
Midterm examination	20%
Mini-project or practical exam	35%

Knowledge domain

D09 Law, safety and risk D11 Artificial intelligence and computer vision D18 Management, innovation and professional practice

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
UNESCO. (2021). Recommendation on the ethics of artificial intelligence. link