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：

- 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！
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