Type of Credit: Elective
Credit(s)
Number of Students
本課程旨在介紹圖型識別與深度學習之基本概念，相關技術與最新應用，透過基本原理之說明，數學方法之解析，開發工具之介紹，配合論文之研讀與討論，期使學生能獲得此一領域之最新資訊，從而應用於研究課題。
能力項目說明
| 教學週次Course Week | 彈性補充教學週次Flexible Supplemental Instruction Week | 彈性補充教學類別Flexible Supplemental Instruction Type |
|---|---|---|
| Week | 主題與課程內容 | 學習投入時數 |
| Week 1 | Introduction | 6 |
| Machine Learning Book | ||
| A Course in Machine Learning | ||
| Week 2 | Overview of Statistical Pattern Recognition | 6 |
| Basic Math | ||
| Week 3 | Bayesian Decision Rule | 6 |
| Bayesian Decision Rule: General Case | ||
| Week 4 | Multivariate Normal Distribution | 6 |
| Independent Binary Features | ||
| Week 5 | Parameter Estimation--Maximum-likelihood and Bayesian Methods | 6 |
| Week 6 | Parameter Estimation--Maximum-likelihood and Bayesian Methods | 6 |
| PCA | ||
| Week 7 | Markov Chains | 6 |
| Hidden Markov Models | ||
| Week 8 | Non-Parametric Estimation | 6 |
| Nearest Neighbor Rule | ||
| Week 9 | Linear Discriminant Functions | |
| Support Vector Machines | ||
| Gradient Descent | ||
| Week 10 | Dimensionality Reduction: FLD, LPP,ICA | 6 |
| Week 11 | Midterm | 10 |
| Week 12 | Similarity Measure | 6 |
| Clustering | ||
| Feature Selection | ||
| Week 13 | Artificial Neural Networks (ANN) | 6 |
| Multilayer Neural Networks | ||
| Week 14 | Deep Learning: Tutorial | 6 |
| Week 15 | Generative AI, XAI | 6 |
| Week 16 | Final project preparation | 6 |
| Week 17 | Final project preparation | 6 |
| Week 18 | Project Presentation | 12 |
http://www.cs.nccu.edu.tw/~whliao/pr2023/ nccucs/nccucs