Course title
F00410003
Pattern Recognition

 KANZAWA Yuuchi
Course description
Students will learn numerical algorithms for pattern recognition based on optimization problems and implement such the algorithms using C++ programming language.
Purpose of class
Students can apply an adequate algorithm to given pattern recognition problems.
They can also discuss reasonably the obtained results from the applied algorithms.
Goals and objectives
  1. Students can contruct optimization problems from
    given pattern recognition problems,
    as part of developing an engineering sense based on mathematical laws.
  2. Students can apply an adequate algorithm to given pattern recognition problems,
    as part of developing an engineering sense based on mathematical laws.
  3. Students can implement pattern recognition algorithms using C++ programming language,
    as part of developing an engineering sense based on mathematical laws.
  4. Students can discuss reasonably the obtained results from the pattern recognition algorithms,
    as part of developing an engineering sense based on mathematical laws.
Relationship between 'Goals and Objectives' and 'Course Outcomes'

 Report Exam. Total.
1. 10% 15% 25%
2. 10% 15% 25%
3. 10% 15% 25%
4. 10% 15% 25%
Total. 40% 60% -
Evaluation method and criteria
Report (40%) and term-end examination (60%).
Language
Japanese
Class schedule

 Class schedule HW assignments (Including preparation and review of the class.) Amount of Time Required
1. Outline of this class.
Developing matrix class 		Reviewing F0732500:"Information Processing 2" 190minutes
2. Classifying optimization problems Reviewing optimization problems claasification 190minutes
3. Maximum likelihood Reviewing maximum likelihood 190minutes
4. Linear multiple regression analysis Reviewing linear equations solver(LU decomposition) and Linear multiple regression analysis. 190minutes
5. Newton method Reviewing Newton method 190minutes
6. Logistic regression analysis Reviewing Newton method and Logistic regression analysis 190minutes
7. Lagrange multiplier method Reviewing Lagrange multiplier method. 190minutes
8. KKT conditions Reviewing KKT conditions 190minutes
9. Classification based on margin maximization Reviewing classification based on margin maximization 190minutes
10. Active set method Reviewing active set method 190minutes
11. Parameter inference of Gaussian mixture models I: weights and means Reviewing parameter inference of Gaussian mixture models 190minutes
12. Parameter inference of Gaussian mixture models II: covariances Reviewing parameter inference of Gaussian mixture models 190minutes
13. Summarizing this class. Reviewing previous classes. 190minutes
14. End-term examination and its review. Preparing end-term examination 190minutes
Total. - - 2660minutes
Feedback on exams, assignments, etc.
ways of feedback specific contents about "Other"
Feedback in the class
Textbooks and reference materials
recommended through class
Prerequisites
Grade B or greater in "Information Processing 2", or equivalent skill.
Office hours and How to contact professors for questions
  • Wednesday 12:30-13:00
    The lecturer recommends making appointment in advance.
Regionally-oriented
Non-regionally-oriented course
Development of social and professional independence
  • Course that cultivates an ability for utilizing knowledge
Active-learning course
More than one class is interactive
Course by professor with work experience
Work experience Work experience and relevance to the course content if applicable
N/A N/A
Education related SDGs:the Sustainable Development Goals
  • 9.INDUSTRY, INNOVATION AND INFRASTRUCTURE
Last modified : Sat Mar 08 04:20:09 JST 2025