Hiroshima University Syllabus

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Academic Year 2024Year School/Graduate School Common Graduate Courses (Doctoral Course)
Lecture Code 8E550201 Subject Classification Common Graduate Courses
Subject Name Pattern Recognition and Machine Learning
Subject Name
Subject Name in
Pattern Recognition and Machine Learning
ナヌッシュ テンデイル ジョージ
Campus Higashi-Hiroshima Semester/Term 1st-Year,  Second Semester,  4Term
Days, Periods, and Classrooms (4T) Tues9-10,Thur9-10:IAS K110
Lesson Style Lecture Lesson Style
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Credits 2.0 Class Hours/Week   Language of Instruction E : English
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 
Special Subject for Teacher Education   Special Subject  
Class Status
within Educational
(Applicable only to targeted subjects for undergraduate students)
This course is one of the elective subjects in the category of "Career Development and Data Literacy Courses" for Common Graduate Courses. This category of courses aims to provide opportunities for students to learn about the development of the current social systems, to gain knowledge needed for the future, to concretely tackle the challenges facing modern society, and to acquire the ability to utilize knowledge and skills. 
Criterion referenced
(Applicable only to targeted subjects for undergraduate students)
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. Review of R basics.
Statistical learning
Simple Linear regression
Multiple Linear regression
Logistic regression
Linear discriminant analysis
Resampling methods
Linear Model Selection and Regularization
Basis functions, splines
Generalized additive models
Tree-based methods
Support vector machines 1
Support vector machines 2
Unsupervised learning 1 - Principal Component Analysis
Unsupervised learning 2 - Clustering methods 
James, G., Witten, D., Hastie, T., and Tibshinari, R. (2013). An Introduction to Statistical Learning, with Applications in R. Springer. ISBN 978-1-4614-7137-0 
PC or AV used in
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Learning techniques to be incorporated  
Suggestions on
Preparation and
It is best to work regularly. Follow the lectures sequence. Reproduce all examples discussed in the lectures on your own. Work carefully through the assignments. 
Requirements Students should be familiar with R. For this reason, it is recommended that students take Data Science in 3T before taking this course. 
Grading Method Reports and commitment 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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