8E550201 Pattern Recognition and Machine Learning

Hiroshima University Syllabus

Japanese

Academic Year 2022Year School/Graduate School Common Graduate Courses (Doctoral Course)

Lecture Code 8E550201 Subject Classification Common Graduate Courses

Subject Name パターン認識と機械学習

Subject Name
（Katakana）パターンニンシキトキカイガクシュウ

Subject Name in
English Pattern Recognition and Machine Learning

Instructor IMORI SHINPEI

Instructor
(Katakana) イモリ　シンペイ

Campus Higashi-Hiroshima Semester/Term 1st-Year, Second Semester, 4Term

Days, Periods, and Classrooms (4T) Tues9-10,Thur9-10：Online

Lesson Style Lecture Lesson Style
(More Details) 　

Credits 2.0 Class Hours/Week 　 Language of Instruction J : Japanese

Course Level 5 : Graduate Basic

Course Area（Area） 25 : Science and Technology

Course Area（Discipline） 02 : Information Science

Eligible Students

Keywords Pattern recognition, Machine learning, Statistics, R, SDG_04

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program This course is one of the elective subjects, in "Career Development and Data Literacy Courses", Common Graduate Courses that aims to learn about the development of the current social systems, to gain knowledge needed for the future, to specifically tackle the challenges facing modern society and to acquire the ability to use knowledge and skills.
Criterion referenced
Evaluation

Class Objectives
/Class Outline To understand basic methods and concepts related to pattern recognition and machine learning, using the statistical software R.

Class Schedule Introduction
Mathematical preliminaries
Linear regression
Logistic regression
Linear discriminant analysis
Quadratic discriminant analysis
K-nearest neighbors
Support vector machines
Decision trees
Neural networks
Principal component analysis
Clustering
Canonical correlation analysis
Resampling methods
Advanced topics

Text/Reference
Books,etc. James, G, et al. An Introduction to Statistical Learning with Applications in R, Springer.

PC or AV used in
Class,etc. 　

(More Details)

Learning techniques to be incorporated 　

Suggestions on
Preparation and
Review Please review lessons by using handouts

Requirements Although this course is for the beginner, the students in this class should be familiar with R.

Grading Method Reports and attitude to the class

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message

Other

Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching.

Academic Year	2022Year	School/Graduate School	Common Graduate Courses (Doctoral Course)
Lecture Code	8E550201	Subject Classification	Common Graduate Courses
Subject Name	パターン認識と機械学習
Subject Name （Katakana）	パターンニンシキトキカイガクシュウ
Subject Name in English	Pattern Recognition and Machine Learning
Instructor	IMORI SHINPEI
Instructor (Katakana)	イモリ　シンペイ
Campus	Higashi-Hiroshima	Semester/Term	1st-Year, Second Semester, 4Term
Days, Periods, and Classrooms	(4T) Tues9-10,Thur9-10：Online
Lesson Style	Lecture	Lesson Style (More Details)

Credits	2.0	Class Hours/Week		Language of Instruction	J : Japanese
Course Level	5 : Graduate Basic
Course Area（Area）	25 : Science and Technology
Course Area（Discipline）	02 : Information Science
Eligible Students
Keywords	Pattern recognition, Machine learning, Statistics, R, SDG_04
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program	This course is one of the elective subjects, in "Career Development and Data Literacy Courses", Common Graduate Courses that aims to learn about the development of the current social systems, to gain knowledge needed for the future, to specifically tackle the challenges facing modern society and to acquire the ability to use knowledge and skills.
Criterion referenced Evaluation
Class Objectives /Class Outline	To understand basic methods and concepts related to pattern recognition and machine learning, using the statistical software R.
Class Schedule	Introduction Mathematical preliminaries Linear regression Logistic regression Linear discriminant analysis Quadratic discriminant analysis K-nearest neighbors Support vector machines Decision trees Neural networks Principal component analysis Clustering Canonical correlation analysis Resampling methods Advanced topics
Text/Reference Books,etc.	James, G, et al. An Introduction to Statistical Learning with Applications in R, Springer.
PC or AV used in Class,etc.
(More Details)
Learning techniques to be incorporated
Suggestions on Preparation and Review	Please review lessons by using handouts
Requirements	Although this course is for the beginner, the students in this class should be familiar with R.
Grading Method	Reports and attitude to the class
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message
Other
Please fill in the class improvement questionnaire which is carried out on all classes. Instructors will reflect on your feedback and utilize the information for improving their teaching.

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