| Course title | |||||
| パターン認識と機械学習 [Pattern Recognition and Machine Learning] | |||||
| Course category | technology speciality courses | Requirement | Credit | 2 | |
| Department | Year | 3～4 | Semester | 1st | |
| Course type | 1st | Course code | 023662 | ||
| Instructor(s) | |||||
| 堀田 政二 [HOTTA Seiji] | |||||
| Facility affiliation | Faculty of Engineering | Office | Email address | ||
| Course description |
| Pattern recognition classifies, identifies or recognizes symbols, structures or any type of information represented, conveyed or even hidden in a set of signals which are redundant and often noisy. It is theoretically and scientifically important to learn human abilities of pattern recognition and practically important to realize smooth human machine interaction. |
| Expected Learning |
| Refer to the curriculum polices shown in the TUAT web site. |
| Course schedule |
|
1. What are patterns, what is pattern recognition? 2. Human Pattern Recognition, Findings from Pathology, Neurophysiology and Cognitive Science 3. Pattern Recognition by Computers 4. Discriminant Functions 5. Introduction to Statistical Methods 6. Details in Statistical Methods (quadratic discriminant function, and modified quadratic discriminant function) 7. Clustering and Prototype Learning 8. Neural Networks 9. Convolutional NN and Recurrent NN 10. Elastic Matching 11. Practical Issues (Normalization, Feature extraction, Feature selection) 12. Practice of Handwritten Character Recognition 13. Structural Methods and Typical Examples 14. Context Processing 15. Summary and examination |
| Prerequisites |
|
Mathematics In addition to 30 hours that students spend in the class, students are recommended to prepare for and revise the lectures, spending the standard amount of time as specified by the University and using the lecture handouts as well as the references specified below |
| Required Text(s) and Materials |
| References |
| Duda, Hart, Stork: Pattern Classification, John Wiley & Sons. |
| Assessment/Grading |
| Participation 30%, Essays twice 10% and Examination 60% |
| Message from instructor(s) |
| Course keywords |
| Office hours |
| Remarks 1 |
| Remarks 2 |
| Related URL |
| class code: wn2wwzi |
| Lecture Language |
| Language Subject |
| Last update |
| 4/6/2022 7:41:10 AM |