機器學習概論
Introduction to Machine Learning
| 節 | 週二 |
|---|---|
5 13:20–14:10 | 機器學習概論 EC022（光復） 3 節連堂 |
6 14:20–15:10 | |
7 15:30–16:20 |
* 根據陽明交大上課時間表所列
This course introduces the foundations and applications of machine learning, from classical models (regression, classification, ensemble, kernel methods, clustering) to modern deep learning (CNNs, RNNs, transformers, GANs, diffusion). Students will learn both theoretical concepts and practical skills to implement, evaluate, and apply machine learning models using Python and modern frameworks.
Linear algebra, probability & statistics, calculus, programming (Python), and basic deep learning frameworks (such as PyTorch, TensorFlow, or Keras).
無備註
Yian (Ed) Chang 張翊鞍 (Email: edchang888.cs14@nycu.edu.tw) Ming-Xian (Alan) Zhuang 莊明憲 (Email: mxzhuang.cs14@nycu.edu.tw)
* This is a newly offered course, and the grading scheme will be adjusted. I will do my best to ensure that students can complete the course smoothly. Homework: 30% Attendance / In-class Quizzes: 15% Midterm Exam 1: 15% Midterm Exam 2: 15% Final Project: 25%
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| 週次 | 主題 |
|---|---|
| 第 1 週 | Introduction to Machine Learning |
| 第 2 週 | Linear Regression & Optimization Basics |
| 第 3 週 | Bias–Variance Tradeoff & Model Evaluation |
| 第 4 週 | Regularization & Logistic Regression |
| 第 5 週 | Decision Trees & Ensemble Methods |
| 第 6 週 | Kernel Methods & SVM |
| 第 7 週 | Dimensionality Reduction / Midterm 1 |
| 第 8 週 | Clustering & EM Algorithm |
| 第 9 週 | Neural Networks (MLP) |
| 第 10 週 | Convolutional Neural Networks (CNN) |
| 第 11 週 | Sequence Models |
| 第 12 週 | Transformers / Midterm 2 |
| 第 13 週 | Generative Models — GAN |
| 第 14 週 | Generative Models — Diffusion |
| 第 15 週 | Multimodal & LLM Applications / Final Project Presentation |
| 第 16 週 | Course Summary & ML Roadmap / Final Project Presentation |
1. C. Bishop, Pattern Recognition and Machine Learning, Springer 2006 https://www.springer.com/gp/book/9780387310732 Free pdf download: https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf 2. Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep Learning, MIT Press, 2016 Free pdf download: https://www.deeplearningbook.org/
- 地點
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- 時間
- 4:20~5:20 pm on Tuesdays at EC241B; other time slots: email me first
- 聯絡方式
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