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2017　Pattern Recognition

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Academic unit or major
Undergraduate major in Computer Science
Instructor(s)
Shimosaka Masamichi  Shinoda Koichi 
Class Format
Lecture     
Media-enhanced courses
Day/Period(Room No.)
Mon5-6(W621)  Thr5-6(W621)  
Group
-
Course number
CSC.T352
Credits
2
Academic year
2017
Offered quarter
2Q
Syllabus updated
2017/3/22
Lecture notes updated
-
Language used
Japanese
Access Index

Course description and aims

This course covers the mathematical fundamentals of pattern recognition with generative models.

Student learning outcomes

At the end of the course, students will be able to explain the basic concept of the pattern recognition with generative models, understand mathematics to describe the generative models, and implement the models explained in the lecture.

Keywords

Pattern recognition, Statistical machine learning, Generative models, Maximum likelihood estimation, Bayesian inference

Competencies that will be developed

✔ Specialist skills Intercultural skills Communication skills Critical thinking skills Practical and/or problem-solving skills

Class flow

Before coming to class, students should read the course schedule and check what topics will be covered. Required learning should be completed
outside of the classroom for preparation and review purposes.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Overview of pattern recognition Peruse chapter 1 of the course textbook before coming to class.
Class 2 Basics in statistical pattern recognition Peruse chapter 1 of the course textbook before coming to class.
Class 3 Criteria for discriminative functions Peruse chapter 3 of the course textbook before coming to class.
Class 4 Maximum likelihood estimation Peruse chapter 4 of the course textbook before coming to class.
Class 5 OCR recognition using linear discriminant analysis 1 Peruse chapter 6 of the course textbook before coming to class.
Class 6 Model selection in maximum likelihood estimation Peruse chapter 7 of the course textbook before coming to class.
Class 7 Theoretical analysis of maximum likelihood estimation Peruse chapter 5 of the course textbook before coming to class.
Class 8 Mixture models and maximum likelihood estimation in mixture models Peruse chapter 8 of the course textbook before coming to class.
Class 9 OCR recognition using linear discriminant analysis 2 Peruse chapter 2 of the course textbook before coming to class.
Class 10 Bayesian inference Peruse chapter 9 of the course textbook before coming to class.
Class 11 Computation in Bayesian inference Peruse chapter 10 of the course textbook before coming to class.
Class 12 Model selection and approximate inference in Bayesian inference Peruse chapter 11 of the course textbook before coming to class.
Class 13 Kernel density estimation Peruse chapter 12 of the course textbook before coming to class.
Class 14 K-nearest neighbor density estimation Peruse chapter 13 of the course textbook before coming to class.
Class 15 Summary and exercises of generative pattern recognition Review all the chapters of the course textbook

Textbook(s)

Textbook about pattern recognition will be introduced in the class

Reference books, course materials, etc.

Pattern Recognition and Machine Learning (Information Science and Statistics), Christopher Bishop, Springer.

Assessment criteria and methods

Course scores are based on the final examination (70%) and the participation to the lecture (30%).
The participation to the lecture will be assessed on exercise problems during class etc.

Related courses

  • ZUS.F301 ： Foundations of Functional Analysis
  • CSC.T242 ： Probability Theory and Statistics
  • CSC.T272 ： Artificial Intelligence
  • CSC.T353 ： Biological Data Analysis

Prerequisites (i.e., required knowledge, skills, courses, etc.)

No prerequisites

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