圖形識別(英文授課)

The goal of this course is to introduce students to pattern recognition. Pattern recognition is a research area that deals with recognizing patterns in data based on their features. Students should learn the principles and issues involved in designing a pattern recognition system to solve real-world problems.

calculus, linear algebra, computer programming, some basic knowledge in probability / statistics

無備註

eCampus

3 individual programming assignments, 2 exams

• Probablistic Pattern Classification

  Bayesian classifiers Gaussian distribution Probability density functions Nonparametric methods

• Evaluation and Generalization

• More on Building Classifiers

  Linear classifiers, MSE Perceptron algorithm Multi-layer perceptrons RBF networks Support vector machines

• Feature Selection and Dimensionality Reduction

  Sequential feature selection Linear discriminant analysis Principle component analysis

• Additional Topics on Classification

  Hidden Markov models Combining classifiers

• Clustering / Unsupervised Learning

  Clustering basics Clustering algorithms Clustering validity Cluster ensembles Self-organizing feature maps

• Introduction

Pattern Recognition (4th Edition), by S. Theodoridis and K. Koutroumbas, Academic Press

地點

EC709

時間

2DX

聯絡方式

phone: 56689, e-mail: wangts@cs.nctu.edu.tw