| 當期課號 |
7766 |
Course Number |
7766 |
| 授課教師 |
鄭文昌 |
Instructor |
CHENG,WEN CHANG |
| 中文課名 |
圖形識別 |
Course Name |
Pattern Recognition |
| 開課單位 |
資訊工程系碩士在職專班一A |
Department |
|
| 修習別 |
選修 |
Required/Elective |
Elective |
| 學分數 |
3 |
Credits |
3 |
| 課程目標 |
本課程主要介紹圖形識別的知識，學生在本課程將可了解相關概念有：貝氏定理分類器，線性與非線性分類器，特徵挑選，特徵產生，脈絡相關分類，系統評估，分群演算法 |
Objectives |
The goal of this course is to provide the students with a basic knowledge of pattern recognition. The students will realize the following concepts in the course:
1.Classifiers based on Bayes decision theory
2.Linear/nonlinear classifiers
3.Feature selection
4.Feature generation
5.Context-dependent classification
6.System evaluation
7.Clustering algorithms |
| 教材 |
1. MIT開放課程
2. Christopher M. Bishop, "Pattern Recognition and Machine Learning", Springer, 2006. |
Teaching Materials |
1. MIT開放課程
2. Christopher M. Bishop, "Pattern Recognition and Machine Learning", Springer, 2006. |
| 成績評量方式 |
作業，專題，期中考，期末考 |
Grading |
Homeworks、projects、midterm and final exams。 |
| 教師網頁 |
|
| 教學內容 |
迴歸與分類
主動學習
特徵選擇
密度估計
分群
模型選擇
推理
模型與方法：
線性迴歸、可加性模型
廣義線性模型
類神經網路
支援向量機(SVM)
提升(Boosting)演算法
混和模型、專家混和模型
核密度估計
馬可夫鏈/過程
隱藏式馬可夫模型 (HMM)
貝式網路, 馬可夫隨機域 |
Syllabus |
Regression and classification
Active learning
Feature selection
Density estimation
Clustering
Model selection
Inference
Models and methods:
linear regression, additive models
Generalized Linear Models
Neural networks
Support Vector Machine (SVM)
Boosting
Mixture models, mixtures of experts
Kernel density estimation
Markov chain/processes
Hidden Markov Models (HMM)
Belief networks, Markov random fields |