Web Syllabus

(2019)

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Pattern Recognition Technology

 YAMANE, Ken
  Elective  2 credits
【Information and Electronic Engineering･1st semester】
19-1-1751-3192

１．
Outline
The main topics are as follows: linear models for regression, linear models for classification, neural networks.
２．
Objectives
The aim of the course is to learn fundamental methods and techniques of pattern recognition.
３．
Grading Policy
Students are evaluated with reports in each lecture (30%), a mid-term exam (30%), and a term exam (40%).
４．
Textbook and Reference
No textbook is used.
５．
Requirements (Assignments)
Students need to use LMS.
６．
Note
７．
Schedule
１. Introduction
２. Template matching, prototype, nearest-neighbor method
３. Least squares method
４. Linear discriminant, linear regression, logistic regression
５. Bayesian inference, maximum likelihood
６. Clustering: k-means clustering, Gaussian mixture model (GMM), expectation-maximization algorithm (EM algorithm)
７. Time series pattern recognition: matching algorithm based on dynamic programming (DP matching), hidden Markov model (HMM)
８. Summary, mid-term exam
９. Radial basis function network (RBFN)
10. Support vector machine (SVM), support vector regression (SVR)
11. Neural networks I: recurrent neural network, Hopfield model
12. Neural networks II: perceptron, leaning rules
13. Neural networks III: Multi-layer neural network (Multilayer perceptron, MLP), backpropagation algorithm, deep neural network (DNN)
14. State-of-the-art technology: Deep learning (CNN, R-CNN, fast R-CNN, faster R-CNN, mask R-CNN, etc.)
15. Summary, term exam
１．
Outline
The main topics are as follows: linear models for regression, linear models for classification, neural networks.
２．
Objectives
The aim of the course is to learn fundamental methods and techniques of pattern recognition.
３．
Grading Policy
Students are evaluated with reports in each lecture (30%), a mid-term exam (30%), and a term exam (40%).
４．
Textbook and Reference
No textbook is used.
５．
Requirements (Assignments)
Students need to use LMS.
６．
Note
７．
Schedule
１. Introduction
２. Template matching, prototype, nearest-neighbor method
３. Least squares method
４. Linear discriminant, linear regression, logistic regression
５. Bayesian inference, maximum likelihood
６. Clustering: k-means clustering, Gaussian mixture model (GMM), expectation-maximization algorithm (EM algorithm)
７. Time series pattern recognition: matching algorithm based on dynamic programming (DP matching), hidden Markov model (HMM)
８. Summary, mid-term exam
９. Radial basis function network (RBFN)
10. Support vector machine (SVM), support vector regression (SVR)
11. Neural networks I: recurrent neural network, Hopfield model
12. Neural networks II: perceptron, leaning rules
13. Neural networks III: Multi-layer neural network (Multilayer perceptron, MLP), backpropagation algorithm, deep neural network (DNN)
14. State-of-the-art technology: Deep learning (CNN, R-CNN, fast R-CNN, faster R-CNN, mask R-CNN, etc.)
15. Summary, term exam