| Relevance of Course Objectives and Core Learning Outcomes(%) |
Teaching and Assessment Methods for Course Objectives |
| Course Objectives |
Competency Indicators |
Ratio(%) |
Teaching Methods |
Assessment Methods |
1. Understand the theoretical foundations of machine learning algorithms.
2. Implement machine learning algorithms using Python and TensorFlow.
3. Apply machine learning algorithms to real-world problems. |
| 1.Having abilities on computer science literacy, information theory, and mathematical analysis. |
|
|
| Exercises |
| Discussion |
| Lecturing |
| topic Discussion/Production |
|
| Written Presentation |
| Attendance |
| Assignment |
| Quiz |
|
| Course Content and Homework/Schedule/Tests Schedule |
| Week |
Course Content |
| Week 1 |
What is Machine Learning |
| Week 2 |
End-to-End Machine Learning Task |
| Week 3 |
Classification |
| Week 4 |
Regression |
| Week 5 |
Gradient Descent |
| Week 6 |
Support Vector Machine |
| Week 7 |
Decision Tree |
| Week 8 |
Midterm Exam |
| Week 9 |
Ensemble Learning and Random Forest
|
| Week 10 |
Data Preprocessing |
| Week 11 |
Dimensionality Reduction
|
| Week 12 |
Unsupervised Learning |
| Week 13 |
Deep Learning and Neural Network |
| Week 14 |
Deep Learning and Neural Network
|
| Week 15 |
Reinforcement Learning |
| Week 16 |
Final Exam
Reading ML-related papers |
self-directed
learning |
02.Viewing multimedia materials related to industry and academia.
03.Preparing presentations or reports related to industry and academia.
|
|
| Evaluation |
Midterm exam (30%)
Final exam (20%)
In-class exercises (20%)
Final project (20%)
Attendance (10%) |
| Textbook & other References |
Self-made materials
Reference books：Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems
Authors：Aurélien Géron
Publisher：O’REILLY |
| Teaching Aids & Teacher's Website |
| Self-made materials |
| Office Hours |
| Please make an appointment by email |
| Sustainable Development Goals, SDGs(Link URL) |
| include experience courses：N |
|