Artificial Intelligence

Course title

L0026000

Course description

In recent years, artificial intelligence technology has made remarkable progress and is deeply involved not only in academic fields such as information processing and robotics but also in our real society and daily lives. This lecture will cover the fundamental knowledge and theories that form the basis of such artificial intelligence.

Purpose of class

To learn the basic concepts and theories of artificial intelligence and to understand idea of algorithms through examples.

Goals and objectives

To understand search methods. To solve simple examples. (Class schedule 1,2,3)
To understand probabilistic models, programming. To solve simple examples. (Class schedule 4,5,6)
To understand reinforcement learning, state estimation, and machine learning. To solve simple examples. (Class schedule 8,9,10,11,12,13)

Relationship between 'Goals and Objectives' and 'Course Outcomes'

	Check Tests	Midterm Exam	Final Exam	Total.
1.	10%	15%		25%
2.	10%	15%		25%
3.	20%		30%	50%
Total.	40%	30%	30%	-

Evaluation method and criteria

Check tests (40%), midterm exam (30%), and final exam (30%). Over 60% in total is acceptable.
If you understand and explain basic notions introduced in classes, such as search methods, programming, probabilistic models, reinforcement learning, state estimation, and machine learning, and you solve problems whose levels are the same as examples treated in classes, you will be able to achieve a total score of 60% or higher.

Language

Japanese

Class schedule

	Class schedule	HW assignments (Including preparation and review of the class.)	Amount of Time Required
1.	What is Artificial Intelligence? Search 1. (State Space and Search)	Preparation: Read the relevant sections of the reference book and lecture materials in advance.	80minutes
1.		Review: Go through the lecture content and complete the check test.	80minutes
2.	Search 2. (Search for Shortest Path)	Preparation: Read the relevant sections of the reference book and lecture materials in advance.	80minutes
2.	Search 2. (Search for Shortest Path)	Review: Go through the lecture content and complete the check test.	80minutes
3.	Search 3. (Game Theory)	Preparation: Read the relevant sections of the reference book and lecture materials in advance.	80minutes
3.	Search 3. (Game Theory)	Review: Go through the lecture content and complete the check test.	80minutes
4.	Planning and Decision Making 1 (Dynamic Programming)	Preparation: Read the relevant sections of the reference book and lecture materials in advance.	80minutes
4.	Planning and Decision Making 1 (Dynamic Programming)	Review: Go through the lecture content and complete the check test.	80minutes
5.	Stochastic models 1 (fundamentals of probability and Bayesian theory)	Preparation: Read the relevant sections of the reference book and lecture materials in advance.	80minutes
5.		Review: Go through the lecture content and complete the check test.	80minutes
6.	Stochastic Models 2 (Stochastic Generative Models and Naive Bayes)	Preparation: Read the relevant sections of the reference book and lecture materials in advance.	80minutes
6.		Review: Go through the lecture content and complete the check test.	80minutes
7.	Midterm Exam and Comments	Review the contents in Lectures from 1 to 6.	365minutes
8.	Planning and Decision Making 2 (Reinforcement Learning)	Preparation: Read the relevant sections of the reference book and lecture materials in advance.	80minutes
8.	Planning and Decision Making 2 (Reinforcement Learning)	Review: Go through the lecture content and complete the check test.	80minutes
9.	Learning and Recognition 1 (Clustering and Unsupervised Learning)	Preparation: Read the relevant sections of the reference book and lecture materials in advance.	80minutes
9.		Review: Go through the lecture content and complete the check test.	80minutes
10.	Learning and Recognition 2 (Pattern Recognition and Supervised Learning)	Preparation: Read the relevant sections of the reference book and lecture materials in advance.	80minutes
10.		Review: Go through the lecture content and complete the check test.	80minutes
11.	Learning and Recognition 3. (Neural Networks)	Preparation: Read the relevant sections of the reference book and lecture materials in advance.	80minutes
11.	Learning and Recognition 3. (Neural Networks)	Review: Go through the lecture content and complete the check test.	80minutes
12.	State Estimation 1. (Bayesian Filter)	Preparation: Read the relevant sections of the reference book and lecture materials in advance.	80minutes
12.	State Estimation 1. (Bayesian Filter)	Review: Go through the lecture content and complete the check test.	80minutes
13.	State Estimation 2. (Particle Filter)	Preparation: Read the relevant sections of the reference book and lecture materials in advance.	80minutes
13.	State Estimation 2. (Particle Filter)	Review: Go through the lecture content and complete the check test.	80minutes
14.	Final Exam and Comments	Review the contents in Lectures from 8 to 13.	365minutes
Total.	-	-	2650minutes

Feedback on exams, assignments, etc.

ways of feedback	specific contents about "Other"
Feedback in the class

Textbooks and reference materials

TEXTBOOK: T. Taniguchi, “An illustrated Guide to Artificial Intelligence” Kodan-sha
REFERENCE BOOK: Editorial Supervision: S. Honiden, Authors: K. Matsumoto, T. Miyahara, Y. Nagai, R. Ichise, “IT Text Artificial Intelligence”, Ohm-sha

Prerequisites

Recommendation: “Data Structure and Algorithms 2”

Office hours and How to contact professors for questions

During the lunch break on lecture days, or via email.

Regionally-oriented

Non-regionally-oriented course

Development of social and professional independence

Course that cultivates an ability for utilizing knowledge

Active-learning course

N/A

Course by professor with work experience

Work experience	Work experience and relevance to the course content if applicable
N/A	N/A

Education related SDGs:the Sustainable Development Goals

9.INDUSTRY, INNOVATION AND INFRASTRUCTURE
12.RESPONSIBLE CONSUMPTION & PRODUCTION

Last modified : Thu Mar 06 10:05:25 JST 2025