8E550201 Pattern Recognition and Machine Learning

Hiroshima University Syllabus

Japanese

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
（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 NUNES TENDEIRO JORGE

Instructor
(Katakana) 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
(More Details) 　

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
Program
(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
Evaluation
(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

Text/Reference
Books,etc. 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
Class,etc. 　

(More Details)

Learning techniques to be incorporated 　

Suggestions on
Preparation and
Review 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

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	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 （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	NUNES TENDEIRO JORGE
Instructor (Katakana)	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 (More Details)

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 Program (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 Evaluation (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
Text/Reference Books,etc.	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 Class,etc.
(More Details)
Learning techniques to be incorporated
Suggestions on Preparation and Review	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
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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