Hiroshima University Syllabus

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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
Subject Name in
Pattern Recognition and Machine Learning
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
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
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
Canonical correlation analysis
Resampling methods
Advanced topics 
James, G, et al. An Introduction to Statistical Learning with Applications in R, Springer.  
PC or AV used in
(More Details)  
Learning techniques to be incorporated  
Suggestions on
Preparation and
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  
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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