Introduction to Artificial Intelligence

Course title

M0228000

Middle-level Diploma Policy (mDP)

Program / Major	mDP	Goals
(改組前)先進国際課程	A-1	A-1 Students shall obtain basic and advanced knowledge and skills in mathematics, natural and computer sciences as well as presentation skills to communicate on their knowledge with scholars from various fields.
先進国際課程	A-1	A-1 Students shall obtain basic and advanced knowledge and skills in mathematics, natural and computer sciences as well as presentation skills to communicate on their knowledge with scholars from various fields.
先進国際課程	A-2	A-2 To suitably lead an international team in the future, students will be able to consider and make decisions on issues in various kinds of problems by grasping what kind of problems are tackled to solve in what way in a wide range of fields in science and technology.
(改組前)先進国際課程	A-2	A-2 To suitably lead an international team in the future, students will be able to consider and make decisions on issues in various kinds of problems by grasping what kind of problems are tackled to solve in what way in a wide range of fields in science and technology.
先進国際課程	C	C　Ability to make ethical decisions and practice ethically as an engineer who contributes to society.
(改組前)先進国際課程	C	C　Ability to make ethical decisions and practice ethically as an engineer who contributes to society.

Purpose of class

Nowadays, Artificial Intelligence is entering our daily lives. The understanding of how it works, and the impacts on the society, is crucial in order to face the challenges that will come in the near future. This course gives the basic notions for this purpose.

Course description

The course offers an introduction to Artificial Intelligence at a entry level that can be understood by students who are not familiar with programming. The course will cover the main Machine Learning techniques such as Neural Networks, and illustrate the ethical aspects of AI.

Goals and objectives

Students can explain the theory behind AI and cognition
Students can explain the main techniques of Machine Learning
Students can explain the ethical aspects in AI

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

	Exam	Group work	active participation	Total.
1.	16%	11%	7%	34%
2.	15%	11%	7%	33%
3.	15%	11%	7%	33%
Total.	46%	33%	21%	-

Evaluation method and criteria

Evaluation method: exam (46%), group work (33%), active participation (21%)
Criteria: at least 60% of total evaluation is required to pass.
The main exam consists in a quiz of True/False questions. The score is integrated by a group work, which is presented in the last weeks.
Active participation in class, such as in Q＆A sessions, is also counted.

Language

English

Class schedule

	Class schedule	HW assignments (Including preparation and review of the class.)	Amount of Time Required
1.	History of AI	Revise slides and other materials	190minutes
2.	Agents, robots and cognition	Revise slides and other materials	190minutes
3.	Machine Learning types	Revise slides and other materials	190minutes
4.	Feature space	Revise slides and other materials	190minutes
5.	Classifiers	Revise slides and other materials	190minutes
6.	Neural Networks	Revise slides and other materials	190minutes
7.	Introduction to Deep Learning; Adversarial Learning	Revise slides and other materials	190minutes
8.	Guest lecture on future perspectives of AI	Revise slides and other materials	100minutes
8.	Guest lecture on future perspectives of AI	Group work	90minutes
9.	Legal aspects in AI	Revise slides and other materials	100minutes
		Prepare for test	180minutes
		Group work	90minutes
10.	Machine Ethics	Revise slides and other materials	60minutes
		Prepare for test	140minutes
		Group work	90minutes
11.	Moral dilemmas	Revise slides and other materials	60minutes
		Prepare for test	140minutes
		Group work	90minutes
12.	Exam (quiz in computer room) and review	Prepare group presentation	190minutes
13.	Group work presentations (I)	Evaluate other groups	20minutes
13.	Group work presentations (I)	Prepare group presentation	170minutes
14.	Group work presentations (I)	Evaluate other groups	20minutes
Total.	-	-	2870minutes

Feedback on exams, assignments, etc.

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

Textbooks and reference materials

Lectures’ material provided in class (pdf). Reference: Ethem Alpaydin, Machine Learning. MIT Press. 2016

Prerequisites

None

Office hours and How to contact professors for questions

Office hours: Friday noon, by appointment (gabu@shibaura-it.ac.jp)

Regionally-oriented

Non-regionally-oriented course

Development of social and professional independence

Course that cultivates an ability for utilizing knowledge

Active-learning course

More than one class is interactive

Course by professor with work experience

Work experience	Work experience and relevance to the course content if applicable
Applicable	Took part in videogame development, and seen how AI is conceived [Firaxis Games]

Education related SDGs:the Sustainable Development Goals

9.INDUSTRY, INNOVATION AND INFRASTRUCTURE

Last modified : Sat Mar 14 13:49:59 JST 2026