Academics
Management Science and Engineering
Pattern Recognition
Release Date: 2017-09-21| Course Name | Pattern Recognition | Course Number | ||||||||
| Course Semester | 3 | Course Time | 36 | Credit | 2 | |||||
| Course Type | □ Obligatory Courses √ Elective Courses | |||||||||
| Institute | Information science school | Applicable Major | Management science and engineering | |||||||
| Assessment method | □ Examination √ Test | |||||||||
| Advanced courses | Probability theory and mathematical statistics, Image processing | |||||||||
| Teacher | Yang Zhihua | |||||||||
| Teaching Objectives | ||||||||||
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This is an elective course for graduate students majoring in management science and engineering. This course will introduce the basic principle, methods and applications of pattern recognition. The main aims of this course include: 1. Students will be able to know well the fundamental concept, elements and analytic procedure as well as the classical algorithms. 2. Developing the students ability to analyze and solve problems using the theory and methods of pattern recognition. 3. Students will have the ability to design simple pattern recognition system, which will be the foundation for further researching in the field. |
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| Teaching Contents | ||||||||||
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Chapter 1 Introduction: 1. Background, fundamental problems, methods and applications; 2. An introduction of MATLAB; Chapter 2 Feature selecting and optimizing : 1. The fundamental problem of feature selecting; 2. The elements of feature selecting; 3. The main methods on reducing the dimensionality of feature; 4. Feature analysis and assess; 5. Example analysis; Chapter 3 Minimum distance classifier : 1. Distance measurements; 2. Minimum distance classifier and its variants; Chapter 4 Bayes classifier : 1. The element of Bayes classification; 2. Minimum Bayes error decision rule; 3. Minimum Bayes risk decision rule; 4. Example analysis; Chapter 5 Linear discriminant function : 1. The basic idea of linear discriminant function; 2. Fish linear discriminant function and its realization; Chapter 6 Artificial neural networks : 1. Background; 2. Feedforward neural network and its training algorithms; 3. BP algorithm and its MATLAB functions; 5. Example analysis; Chapter 7 Support vector machine : 1. Linearly separable problems and linearly non-separable problems; 2. Algorithms to find the optimal separating hyperplane; 3. Some MATLAB functions on SVM; 4. Example analysis |
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| Outline Designer | Yang Zhihua | Date | 2015.10.10 | |||||||