Kobe Institute of Computing, Syllabus 2023

1. Course Code2287
2. Course Title
G91e: Artificial Intelligence
3. Teacher
KOSHIO, Atsushi
4. TermSpring 2
5. Course Requirements (Courses / Knowledge for this course) and Important Information
Students should have reached the learning objectives of "Fundamentals of Computer
Programming Python".

6. Course Overview and Objectives
Purpose of the course
The purpose of this course is to deepen students' understanding of AI-related technologies,
which have a great impact on ICT and software development in recent years, and to thinkabout their social implementation. In this course, students will learn to design AI systems
and projects by improving their technical insight and experiencing AI development throughhands-on lectures.
Course Outline
The lectures consist of inputs on technological trends and basic principles, hands-on
experience in building machine learning systems through practical lectures on AI
development, and outputs in the form of AI system/project design. The lecture will start withan explanation of the latest trends and concepts in machine learning, and an introduction totheoretical foundations and papers, followed by a case study of an actual projectapplication. In the practical training, students will implement AI using Google Colaboratry toimplement image recognition and natural language processing, and in the AI system project
design, students will develop specifications for AI software in their area of interest, create
an implementation plan, and study business models.
7. Course Outline
1 Introduction to AI
2 Case studies of Frontier of AI aplication
3 Workshop on Potential Applications of Artificial Intelligence
4 Fundamentals of Data Science
5 Fundamentals of Data Science Practice
6 Fundamentals of Deep Learning
7 Implementation of Image Recognition Using Deep Learning
8 Fundamentals of NLP and LLM
9 Practical usage of LLM
10 AI System Design
11 Designing AI Systems
12 AI project design
13 AI project design Practice
14 Presentation of AI application 1
15 Presentation of AI application 216
8. Textbooks (Required Books for this course)
None in particular

9. Reference Books (optional books for further study)
I'll give you instructions during the lecture.
10. Course Goals (Attainment Targets)(1) Be able to determine the scope of application of AI(2) Be able to perform basic data science analysis(3) Understand the fundamentals of deep learning and be able to perform simple
implementations(4) Understand the fundamentals of natural language processing and be able to perform
simple implementations(5) To be able to create specifications for AI systems(6) To be able to consider business models for AI systems(7)(8)
11. Correspondence relationship between Educational goals and Course goals
Educational goals of the school
Course Goals
High level ICT Basic academic skills(1)(2)
Specialized knowledge and literacy(3)(4)
skillsAbility to continually improve own strengths
Problem setting(1)Ability to discover andHypothesis planning(1)
Human skill resolve the problem inHypothesis testing(2)(TankyusocietyPractice(5)(6)
skill）
Fundamental
Ability to step forward(1)
Ability to think through(5)(6)
Competencies for
Ability to work in a teamWorking Persons
Professional ethics
12. Evaluation
Goals
Evaluation method & point allocation
Examination
Quiz
Reports Presentation Deliverables
Other
(1)
〇(2)
〇〇
(3)
〇(4)
〇(5)
〇(6)
〇(7)
(8)
Allocation
10404010
13. Evaluation Criteria
Examination
Quiz
Reports

Able to perform basic data science analysis and execute the learned
analysis appropriately.

Presentation

Be able to plan AI system specifications, implementation plans, andbusiness model plans based on your own issues.

Deliverables

Be able to appropriately execute the codes learned in the lecture on image
recognition and natural language processing.

Other14. Active Learning
Hourly percentage of active learning within the whole class time

50%
1 Active learning such as problem solving assignment using theknowledge and skills acquired in class.

All the time

2 Active learning such as group works and discussions.

Sometimes

3 Outcome presentations and feedbacks.

All the time

4 Students actively make decisions on how the class should be conducted. All the time
15. Notes

16. Course plan(Notice) This plan is tentative and might be changed at the time of delivery

Lessen 1: Introduction to AI

lecture 60 min. Exercise
30 min.
An overview of the current state of AI, including the scope of AI research, explanations ofeach area, and the current state of the machine learning field, will be provided, followed byexercises to deepen understanding.

Lessen 2: Case studies of AI state-of-the-art

lecture 60 min. Exercise30 min.
Case analysis of AI will be conducted while providing cutting-edge examples of AIapplications. In the second half, students will understand the scope of AI applications byconducting their own case analysis.

Lessen 3:Workshop on Potential Applications of Artificial
Exercise 90 min.
Intelligence
Following the content of one or two lectures, we will conduct an exercise to consider thepossibility of using AI. In the first half of the class, we will collaborate in group work. In thesecond half, students will discuss their own ideas for utilization.

Lessen 4: Intrroduction to Data Science

Lecture 90 min.

Learn key concepts in data science, methods of data processing, multivariate analysis,clustering, neural networks, and other concepts and theories.

Lessen 5: Introduction to Data Science Practice

Exercise 90 min.

By actually practicing the content of the fourth session, students will be able to consolidate
their knowledge and deepen their practical understanding.

Lessen 6: Fundamentals of Deep Learning

Lecture 90 min.

This lecture will introduce examples of deep learning applications, explain the algorithm
structure in simple terms, and deepen the technical understanding. The procedures forimplementing deep learning will also be explained.

Lessen 7: Implementation of image analysis using deep learning

Exercise 90 min.

Using Google Colaboraty, image analysis is actually performed.

Lessen 8: Fundamentals of NLP and LLM

Lecture 90 min.

To explain the history of natural language processing and the basics of natural language
processing and Large Language Model using deep learning. To explain the basic analysis
techniques and terminology of natural language processing, and theories related to LLM.

Lessen 9: Practical usage of LLM

Exercise 90 min.

Using actual data and case, we will actually perform Large Language Model for several
tasks using Google Colaboraty.

Lessen 10: Designing AI systems

Lecture 90 min.

This course explains how to formulate specifications, draw system structure diagrams, andimplement the actual system required for designing AI systems.

Lessen 11: AI system design practice

Exercise 90 min.

Conceptualize and develop specifications for an AI-based system for your own issues.

Lessen 12: Designing AI Projects

Lecture 90 min.

While explaining the frameworks that are effective for designing AI projects, we will discuss
points to consider when implementing them.

Lessen 13: Designing AI project practice

Exercise 90 min.

Create a structure for your own project, using a framework that is effective for designing AIprojects.

Lessen 14: Presentation of AI system design 1

Presentation 90 min.

Present and discuss the AI system project outline built through 10-13 lectures andassinment.

Lessen 15: Presentation of AI system design 1

Presentation 90 min.

Present and discuss the AI system project outline built through 10-13 lectures andassinment.