KA240201 Machine Learning

Hiroshima University Syllabus

Japanese

Academic Year 2024Year School/Graduate School School of Informatics and Data Science

Lecture Code KA240201 Subject Classification Specialized Education

Subject Name 機械学習

Subject Name
（Katakana）キカイガクシュウ

Subject Name in
English Machine Learning

Instructor FUKUSHIMA MAKOTO

Instructor
(Katakana) フクシマ　マコト

Campus Higashi-Hiroshima Semester/Term 2nd-Year, Second Semester, 3Term

Days, Periods, and Classrooms (3T) Weds1-4：EDU K201

Lesson Style Lecture Lesson Style
(More Details) 　

Lectures and Exercises

Credits 2.0 Class Hours/Week 　 Language of Instruction B : Japanese／English

Course Level 3 : Undergraduate High-Intermediate

Course Area（Area） 25 : Science and Technology

Course Area（Discipline） 02 : Information Science

Eligible Students

Keywords Pattern Recognition, Machine Leaning

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students) This course is positioned as a machine learning course with an emphasis on classroom lectures and manual calculations.
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) Computer Science Program
（Knowledge and Understanding）
・D1. Knowledge and ability to understand the theoretical framework underlying computer science and to collect and process high-dimensional data through full use of information processing technology based on scientific logic.

Data Science Program
（Knowledge and Understanding）
・D1. Knowledge and ability to understand the theoretical framework of statistics and data analysis and to analyze qualitative/quantitative information of big data accurately and efficiently.
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.
・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis.

Intelligence Science Program
（Knowledge and Understanding）
・D1. A deep systematic understanding of the advanced intelligence of human beings and its realization by computers.
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.

Class Objectives
/Class Outline In this course, students learn about pattern recognition and machine learning. The goal is to acquire the basic knowledge and techniques necessary to understand various machine learning methods.

Class Schedule Lesson 1: Course Orientation/Introduction 1
Lesson 2: Introduction 2
Lesson 3: Probability Distributions 1
Lesson 4: Probability Distributions 2
Lesson 5: Linear Models for Regression 1
Lesson 6: Linear Models for Regression 2
Lesson 7: Linear Models for Classification 1
Lesson 8: Linear Models for Classification 2
Lesson 9: Graphical Models 1
Lesson 10: Graphical Models 2
Lesson 11: Mixture Models and EM 1
Lesson 12: Mixture Models and EM 2
Lesson 13: Continuous Latent Variables 1
Lesson 14: Continuous Latent Variables 2
Lesson 15: Sampling Methods

Assignments in Lessons 4, 6, 8, 10, 12, and 14

Text/Reference
Books,etc. (Reference) Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.

PC or AV used in
Class,etc. 　

(More Details) Lecture materials will be made available through Microsoft Teams.

Learning techniques to be incorporated 　

Suggestions on
Preparation and
Review Use the lecture materials for preparation and review.

Requirements The instructor assumes that students have already learned the basics of calculus, linear algebra, and probability theory. Students who will take the Neural Networks course in the first term of the third year should also take this Machine Learning course.

Grading Method Evaluation is based on the grades of the submitted assignments.

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message Some of the lessons may be delivered online or on-demand. The order of Lesson 15 may be moved up. In either case, students will be notified in advance.

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	School of Informatics and Data Science
Lecture Code	KA240201	Subject Classification	Specialized Education
Subject Name	機械学習
Subject Name （Katakana）	キカイガクシュウ
Subject Name in English	Machine Learning
Instructor	FUKUSHIMA MAKOTO
Instructor (Katakana)	フクシマ　マコト
Campus	Higashi-Hiroshima	Semester/Term	2nd-Year, Second Semester, 3Term
Days, Periods, and Classrooms	(3T) Weds1-4：EDU K201
Lesson Style	Lecture	Lesson Style (More Details)
Lectures and Exercises
Credits	2.0	Class Hours/Week		Language of Instruction	B : Japanese／English
Course Level	3 : Undergraduate High-Intermediate
Course Area（Area）	25 : Science and Technology
Course Area（Discipline）	02 : Information Science
Eligible Students
Keywords	Pattern Recognition, Machine Leaning
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)	This course is positioned as a machine learning course with an emphasis on classroom lectures and manual calculations.
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)	Computer Science Program （Knowledge and Understanding）・D1. Knowledge and ability to understand the theoretical framework underlying computer science and to collect and process high-dimensional data through full use of information processing technology based on scientific logic. Data Science Program （Knowledge and Understanding）・D1. Knowledge and ability to understand the theoretical framework of statistics and data analysis and to analyze qualitative/quantitative information of big data accurately and efficiently. （Abilities and Skills）・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value. ・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis. Intelligence Science Program （Knowledge and Understanding）・D1. A deep systematic understanding of the advanced intelligence of human beings and its realization by computers. （Abilities and Skills）・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.
Class Objectives /Class Outline	In this course, students learn about pattern recognition and machine learning. The goal is to acquire the basic knowledge and techniques necessary to understand various machine learning methods.
Class Schedule	Lesson 1: Course Orientation/Introduction 1 Lesson 2: Introduction 2 Lesson 3: Probability Distributions 1 Lesson 4: Probability Distributions 2 Lesson 5: Linear Models for Regression 1 Lesson 6: Linear Models for Regression 2 Lesson 7: Linear Models for Classification 1 Lesson 8: Linear Models for Classification 2 Lesson 9: Graphical Models 1 Lesson 10: Graphical Models 2 Lesson 11: Mixture Models and EM 1 Lesson 12: Mixture Models and EM 2 Lesson 13: Continuous Latent Variables 1 Lesson 14: Continuous Latent Variables 2 Lesson 15: Sampling Methods Assignments in Lessons 4, 6, 8, 10, 12, and 14
Text/Reference Books,etc.	(Reference) Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.
PC or AV used in Class,etc.
(More Details)	Lecture materials will be made available through Microsoft Teams.
Learning techniques to be incorporated
Suggestions on Preparation and Review	Use the lecture materials for preparation and review.
Requirements	The instructor assumes that students have already learned the basics of calculus, linear algebra, and probability theory. Students who will take the Neural Networks course in the first term of the third year should also take this Machine Learning course.
Grading Method	Evaluation is based on the grades of the submitted assignments.
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message	Some of the lessons may be delivered online or on-demand. The order of Lesson 15 may be moved up. In either case, students will be notified in advance.
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.

Back to syllabus main page