Intelligent Information Processing

Course Information

College	Toyama College	Year	2024
Course Title	Intelligent Information Processing
Course Code	0017	Course Category	Specialized / Elective
Class Format	Lecture	Credits	Academic Credit: 2
Department	Control Information Systems Engineering Course	Student Grade	Adv. 1st
Term	Second Semester	Classes per Week	2
Textbook and/or Teaching Materials
Instructor	Akiguchi Shunsuke

Course Objectives

Through this course, understanding of the following will be facilitated.
･The characteristics of Fuzzy logic, Genetic Algorithm, Reinforcement Learning and Neural Network

Rubric

	Ideal Level of Achievement (Very Good)	Standard Level of Achievement (Good)	Unacceptable Level of Achievement (Fail)
Evaluation 1	Clearly understands the overview of Fuzzy logic, and displays the ability to make an advanced program containing fuzzy logic.	Ability to explain the overview and concept of Fuzzy logic, and displays the ability to make a program containing fuzzy logic.	Unable to explain the overview and concept of Fuzzy logic.
Evaluation 2	Clearly understands the overview of Genetic Algorithm, and displays the ability to make an advanced program containing Genetic Algorithm.	Ability to explain the overview and concept of Genetic Algorithm, and displays the ability to make a program containing Genetic Algorithm.	Unable to explain the overview and concept of Genetic Algorithm.
Evaluation 3	Clearly understands the overview of Reinforcement Learning, and displays the ability to make an advanced program containing Reinforcement Learning.	Ability to explain the overview and concept of Reinforcement Learning, and displays the ability to make a program containing Reinforcement Learning.	Unable to explain the overview and concept of Reinforcement Learning.
Evaluation 4	Clearly understands the overview of Neural Network, and displays the ability to make an advanced program containing Neural Network.	Ability to explain the overview and concept of Neural Network, and displays the ability to make a program containing Neural Network.	Unable to explain the overview and concept of Neural Network.

Assigned Department Objectives

ディプロマポリシー B-3 （情報分野について理論的に説明できる。）

JABEE B3 （情報分野について理論的に説明できる）

Teaching Method

Outline:

In this course, you will learn about the principles and fundamental techniques required for Soft Computing.To understand Fuzzy logic, Genetic Algorithm, Reinforcement Learning and Neural Network.

Style:

Student masters this course through lectures and seminar.

Notice:

The recognition of credit requires 60 points or more rating.

Course Plan

			Theme	Goals
2nd Semester
	3rd Quarter
		1st	Guidance	Guidance: Discuss the goals and structure of this course.
		2nd	Soft Computing	Learn about the details of the Soft Computing.
		3rd	Fuzzy logic -1-	Learn about the details of the Fuzzy logic.
		4th	Fuzzy logic -2-	Learn about designing and implementing the Fuzzy logic.
		5th	Exercise	Exercise
		6th	Genetic Algorithm -1-	Learn about the details of the Genetic Algorithm.
		7th	Genetic Algorithm -2-	Learn about designing and implementing the Genetic Algorithm.
		8th	Exercise	Exercise
	4th Quarter
		9th	Reinforcement Learning -1-	Learn about the details of the Reinforcement Learning.
		10th	Reinforcement Learning -2-	Learn about designing and implementing the Reinforcement Learning.
		11th	Exercise	Exercise
		12th	Neural Network -1-	Learn about the details of the Neural Network.
		13th	Neural Network -2-	Learn about designing and implementing the Neural Network.
		14th	Exercise	Exercise
		15th	Final Examination	Final Examination
		16th	Checking the Final Evaluation	Checking the Final Evaluation

Evaluation Method and Weight (%)

	Examination	Presentation	Mutual Evaluations between students	Behavior	Portfolio	Other	Total
Subtotal	70	0	0	0	30	0	100
Basic Ability	0	0	0	0	0	0	0
Technical Ability	70	0	0	0	30	0	100
Interdisciplinary Ability	0	0	0	0	0	0	0