課 程 概 述
Course Description

課程編碼
Course Code
中文課程名稱
Course Name (Chinese)
英文課程名稱
Course Name (English)
總學分數
Credits
總時數
Hours
3105022 模型辨認 Pattern Recognition 3.0 3
中文概述
Chinese Description
1. 類神經網路簡介2. 層狀認知網路3. 競爭學習神經網路4. 適應共振理論5. 聯想記憶6. 特徵萃取
英文概述
English Description
Topics that are covered in the course include: " Bayesian decision theory: the theoretical statistical basis for recognition based on Bayes theorem from probability " Maximum-likelihood and Bayesian parameter estimation: parameters of probability density functions " Nonparametric techniques: Parzen window, k-nearest neighbor " Linear discriminant functions: gradient descent, relaxation, minimum squared-error procedures such as LMS, and support vector machines " Algorithm-independent machine learning " Unsupervised learning and clustering The course is quite mathematical. Students enrolling this class are expected to have a good understanding of probability and random variables, both one-dimensional and multi-dimensional, and a good background in linear algebra as well as calculus. Some of the necessary math will be reviewed at the beginning of the course, but it is only a quick review, not a math course. Grades will be based on homework, tests, and small computer projects.

備註:

  1. 本資料係由本校各教學單位、教務處課務組、進修部教務組、進修學院教務組及計網中心所共同提供!
  2. 本資料僅供參考,正式資料仍以教務處、進修部、進修學院所公佈之書面資料為準。