2025 (Current Year) Faculty Courses School of Computing Undergraduate major in Computer Science
Pattern Recognition
- Academic unit or major
- Undergraduate major in Computer Science
- Instructor(s)
- Masamichi Shimosaka
- Class Format
- Lecture (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 5-6 Mon / 5-6 Thu
- Class
- -
- Course Code
- CSC.T352
- Number of credits
- 200
- Course offered
- 2025
- Offered quarter
- 2Q
- Syllabus updated
- Mar 31, 2025
- Language
- Japanese
Syllabus
Course overview and goals
This course covers the mathematical fundamentals of pattern recognition with generative models.
Course description and aims
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
- 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/Objectives
| Course schedule | Objectives | |
|---|---|---|
| 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. |
Study advice (preparation and review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
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.
Evaluation methods and criteria
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
- CSC.T254 ： Machine Learning
Prerequisites
No prerequisites