模型辨認.
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課程名稱 |
模型辨認 |
3學分 3小時 |
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英文課程名稱 |
Pattern Recognition |
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中文課程概要 |
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 |
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英文課程概要 |
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. |
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