圖形識別概論
Introduction to Pattern Recognition
| 節 | 週三 |
|---|---|
3 10:10–11:00 | 圖形識別概論 ED102 3 節連堂 |
4 11:10–12:00 | |
N 12:20–13:10 |
* 根據陽明交大上課時間表所列
I will introduce several representative algorithms for pattern recognition and generation, including linear models, neural networks, ensemble methods, kernel methods, convolutional neural networks, transformers, mamba, and generative models.
Linear algebra, probability, calculus, programming (such as Python), and deep learning programming (such as PyTorch, Keras, or TensorFlow)
Four Homework Assignments: 30% (= 7.5% x 4) Midterm Exam: 35% Final Exam: 35%
| 週次 | 主題 |
|---|---|
| 第 1 週 | Introduction |
| 第 2 週 | Linear Model for Regression |
| 第 3 週 | Linear Model for Classification |
| 第 4 週 | Neural Networks |
| 第 5 週 | Ensemble Model I |
| 第 6 週 | Ensemble Model II |
| 第 7 週 | Kernel Method I |
| 第 8 週 | Midterm Exam |
| 第 9 週 | Kernel Method II |
| 第 10 週 | Deep Neural Networks (DNN) |
| 第 11 週 | Convolutional Neural Networks (CNN) I |
| 第 12 週 | Convolutional Neural Networks (CNN) II and Transformers I |
| 第 13 週 | Transformers II |
| 第 14 週 | Mamba |
| 第 15 週 | Final Exam |
| 第 16 週 | Diffusion Models |
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
- EC234-C (TA), EC701 (TA), or EC706 (Instructor) Please send us an email in advance to make an appointment, and we will inform you where to have a discussion.
- 時間
- Wednesday 1:00 pm ~ 2:00 pm
- 聯絡方式
- Instructor: Yen-Yu Lin (林彥宇) Email: lin@cs.nycu.edu.tw TAs: Wei-Hsiang Yu (游為翔) Email: weihsiang.yu@gmail.com Yu-Chi Chung (鍾育騏) Email: cs0905555581@gmail.com Yu-Hsuan Tang (湯于萱) Email: yuijw0720@gmail.com