KA239001 Introduction to Artificial Intelligence

Hiroshima University Syllabus

Japanese

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

Lecture Code KA239001 Subject Classification Specialized Education

Subject Name 人工知能概論

Subject Name
（Katakana）ジンコウチノウガイロン

Subject Name in
English Introduction to Artificial Intelligence

Instructor OGURA MASAKI

Instructor
(Katakana) オグラ　マサキ

Campus Higashi-Hiroshima Semester/Term 2nd-Year, First Semester, Intensive

Days, Periods, and Classrooms (Int) Inte：IAS L102

Lesson Style Lecture Lesson Style
(More Details) 　

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

Course Level 2 : Undergraduate Low-Intermediate

Course Area（Area） 25 : Science and Technology

Course Area（Discipline） 02 : Information Science

Eligible Students

Keywords Artificial Intelligence, Search, Probabilistic Models, State Estimation, Learning and Recognition, Language and Logic

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students)
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) Integrated Arts and Sciences
（Knowledge and Understanding）
・Knowledge and understanding of the importance and characteristics of each discipline and basic theoretical framework.

Program of Electrical,Systems and Information Engineering
（Abilities and Skills）
・Concepts, knowledge and methods which are the basis for studies related to electrical, systems, and information engineering.

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.

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）
・D2. Information processing ability and data analysis ability to contribute to the application and development of artificial intelligence and IoT.

Class Objectives
/Class Outline Students will understand the basic concepts and history of artificial intelligence. Students will learn about major areas of artificial intelligence such as search, optimal path finding, game theory, dynamic programming, probability and Bayesian theory, reinforcement learning, filtering, clustering, supervised and unsupervised learning, neural networks, natural language processing, symbolic logic, proofs, and question answering.

Class Schedule Session 1: Creating Artificial Intelligence・State Space and Basic Search
Session 2: Optimal Path Search
Session 3: Game Theory
Session 4: Dynamic Programming
Session 5: Foundations of Probability and Bayesian Theory
Session 6: Probabilistic Generative Models and Naive Bayes
Session 7: Reinforcement Learning
Session 8: Bayesian Filters
Session 9: Particle Filters
Session 10: Clustering and Unsupervised Learning
Session 11: Pattern Recognition and Supervised Learning
Session 12: Neural Networks
Session 13: Natural Language Processing
Session 14: Symbolic Logic
Session 15: Proof and Question-Answering

Text/Reference
Books,etc. イラストで学ぶ人工知能概論改訂第2版 (KS情報科学専門書) 　2020/12/24 谷口忠大 (著)

PC or AV used in
Class,etc. 　

(More Details)

Learning techniques to be incorporated 　

Suggestions on
Preparation and
Review It will be easier to understand the contents of the lecture if you have read the textbook in advance. In addition, the application of the methods introduced in the lecture to actual data will deepen your understanding of each method.

Requirements The lecture assumes knowledge of linear algebra and probability statistics.

Grading Method The total score of multiple reports during the lecture will be used to determine the grade.

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message It is important to build a solid understanding of the basic concepts and history of artificial intelligence. In addition, since the field of artificial intelligence is rapidly advancing, it is important to have an attitude of continuous learning and interest so that you can flexibly adapt to new methods and technologies.

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	KA239001	Subject Classification	Specialized Education
Subject Name	人工知能概論
Subject Name （Katakana）	ジンコウチノウガイロン
Subject Name in English	Introduction to Artificial Intelligence
Instructor	OGURA MASAKI
Instructor (Katakana)	オグラ　マサキ
Campus	Higashi-Hiroshima	Semester/Term	2nd-Year, First Semester, Intensive
Days, Periods, and Classrooms	(Int) Inte：IAS L102
Lesson Style	Lecture	Lesson Style (More Details)

Credits	2.0	Class Hours/Week		Language of Instruction	J : Japanese
Course Level	2 : Undergraduate Low-Intermediate
Course Area（Area）	25 : Science and Technology
Course Area（Discipline）	02 : Information Science
Eligible Students
Keywords	Artificial Intelligence, Search, Probabilistic Models, State Estimation, Learning and Recognition, Language and Logic
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)	Integrated Arts and Sciences （Knowledge and Understanding）・Knowledge and understanding of the importance and characteristics of each discipline and basic theoretical framework. Program of Electrical,Systems and Information Engineering （Abilities and Skills）・Concepts, knowledge and methods which are the basis for studies related to electrical, systems, and information engineering. 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. 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）・D2. Information processing ability and data analysis ability to contribute to the application and development of artificial intelligence and IoT.
Class Objectives /Class Outline	Students will understand the basic concepts and history of artificial intelligence. Students will learn about major areas of artificial intelligence such as search, optimal path finding, game theory, dynamic programming, probability and Bayesian theory, reinforcement learning, filtering, clustering, supervised and unsupervised learning, neural networks, natural language processing, symbolic logic, proofs, and question answering.
Class Schedule	Session 1: Creating Artificial Intelligence・State Space and Basic Search Session 2: Optimal Path Search Session 3: Game Theory Session 4: Dynamic Programming Session 5: Foundations of Probability and Bayesian Theory Session 6: Probabilistic Generative Models and Naive Bayes Session 7: Reinforcement Learning Session 8: Bayesian Filters Session 9: Particle Filters Session 10: Clustering and Unsupervised Learning Session 11: Pattern Recognition and Supervised Learning Session 12: Neural Networks Session 13: Natural Language Processing Session 14: Symbolic Logic Session 15: Proof and Question-Answering
Text/Reference Books,etc.	イラストで学ぶ人工知能概論改訂第2版 (KS情報科学専門書) 　2020/12/24 谷口忠大 (著)
PC or AV used in Class,etc.
(More Details)
Learning techniques to be incorporated
Suggestions on Preparation and Review	It will be easier to understand the contents of the lecture if you have read the textbook in advance. In addition, the application of the methods introduced in the lecture to actual data will deepen your understanding of each method.
Requirements	The lecture assumes knowledge of linear algebra and probability statistics.
Grading Method	The total score of multiple reports during the lecture will be used to determine the grade.
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message	It is important to build a solid understanding of the basic concepts and history of artificial intelligence. In addition, since the field of artificial intelligence is rapidly advancing, it is important to have an attitude of continuous learning and interest so that you can flexibly adapt to new methods and technologies.
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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