Artificial Intelligence

Course title

L0026000

Course description

In recent years, "Artificial Intelligence" has become widely relevant not only to academic fields such as information processing and robotics, but also to our real world and daily lives. This course presents basic ideas and theories of 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, machine learning, and natural language processing. 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 get over 60%.

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 Chapters 1 and 2 in the textbook.	80minutes
1.		Review: Find your own examples and understand “search”.	80minutes
2.	Search 2. (Search for Shortest Path)	Preparation: Read Chap. 3 in the textbook.	80minutes
2.	Search 2. (Search for Shortest Path)	Review: Understand the algorithm of shortest path search.	80minutes
3.	Search 3. (Game Theory)	Preparation: Read Chap. 4 in the textbook.	80minutes
3.	Search 3. (Game Theory)	Review: Find your own examples to deepen your understanding of “game theory”.	80minutes
4.	Planning and Decision Making 1 (Dynamic Programming)	Preparation: Read Chap. 5 in the textbook.	80minutes
4.	Planning and Decision Making 1 (Dynamic Programming)	Review: Find your own examples and deepen your understanding of “dynamic programming”.	80minutes
5.	Stochastic models 1 (fundamentals of probability and Bayesian theory)	Preparation: Read Chap. 6 in the textbook.	80minutes
5.		Review: Find your own examples to deepen your understanding of “Bayesian theory”	80minutes
6.	Stochastic Models 2 (Stochastic Generative Models and Naive Bayes)	Preparation: Read Chap. 7 in the textbook.	80minutes
6.		Review: Find your own examples to deepen your understanding of “stochastic generative models”.	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 Chap. 8 in the textbook.	80minutes
8.	Planning and Decision Making 2 (Reinforcement Learning)	Review: Find your own examples and deepen your understanding of “reinforcement learning”.	80minutes
9.	Learning and Recognition 1 (Clustering and Unsupervised Learning)	Preparation: Read Chap. 11 in the textbook.	80minutes
9.		Review: Find your own examples to deepen your understanding of “unsupervised learning”.	80minutes
10.	Learning and Recognition 2 (Pattern Recognition and Supervised Learning)	Preparation: Read Chap. 12 in the textbook.	80minutes
10.		Review: Find your own examples to deepen your understanding of “supervised learning”.	80minutes
11.	Learning and Recognition 3. (Neural Networks)	Preparation: Read Chap. 13 in the textbook.	80minutes
11.	Learning and Recognition 3. (Neural Networks)	Review: Find your own examples and deepen your understanding of “neural networks”.	80minutes
12.	State Estimation 1. (Bayesian Filter)	Preparation: Read Chap. 9 in the textbook.	80minutes
12.	State Estimation 1. (Bayesian Filter)	Review: Find your own examples and deepen your understanding of “bayesian filters”.	80minutes
13.	State Estimation 2. (Particle Filter)	Preparation: Read Chap. 10 in the textbook.	80minutes
13.	State Estimation 2. (Particle Filter)	Review: Find your own examples and deepen your understanding of “particle filters”.	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

Lunch time between 2nd and 3rd periods on Monday, Or E-mail contact is also available to ask questions.

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 : Sat Mar 08 04:24:20 JST 2025