|Lecturer||Kenichiro ISHII, Professor|
|Department||School of Engineering / Graduate School of Engineering, 2011 Spring|
|Recommended for:||School of Infomatics and Science (3・3 hours / session One session / week 15 weeks / semester)|
The major objectives of this course are, to understand the basic ideas of pattern recognition, and to acquire skills in solving actual problems using classification/learning algorithms.
This course consists of two parts. The first part is a lecture, where not only technological explanation but also some exercise problems are given. The second part is an exercise for solving pattern recognition problems using a computer.
A sufficient knowledge of linear algebra, probability theory and statistics is required. Having some programming skills is preferable in order to perform computer simulations in exercises.
|1||pattern recognition system, feature extraction, feature vector|
|2||prototype, nearest neighbor rule, linear discriminant function|
|3||perceptron learning rule, weight space, solution region|
|4||perceptron convergence theorem, dimension size, sample size|
|5||majority voting, piecewise linear discriminant function|
|6||Widrow-Hoff learning rule, multiple regression analysis|
|7||error estimation and perceptron|
|8||back propagation method, neural network, feature evaluation|
|9||transformation of feature space, Fisher's method|
|11||empirical probability, subjective probability, Bayes theorem|
|12||Bayesian updating, Bayesian estimation|
|13||Bayes decision rule, Bayes error|
|14||parameter estimation by maximum likelihood method|
Grading is based on reports and the final examination.
May 10, 2020