1. Program of Study
General Education, Mahidol University International College
2. Course Code
ICGN134 Introduction to Artificial Intelligence
3. Number of Credits
2 (2-0-4) Credits (Lecture-Lab-Self Learning)
4. Prerequisites
None
5. Type of Course
Elective Course
6. Trimester / Academic year
2/2022-2023
7. Course Conditions
Class size will be in the range of 25-30 students
8. Course Description
The artificial intelligence terminology; machine learning types and techniques; guidelines for building the machine learning model; neural networks; an evaluation in the AI system; the real-world artificial intelligence; the future trends of the artificial intelligence; dangers and threats of the artificial intelligence
9. Course Objectives
After successful completion of this course, students will be able to
1. recognize basic knowledge of Artificial Intelligence
2. understand the essentials of Artificial Intelligence
3. build simple Artificial Intelligence applications and evaluate the performances
4. recognize copyrights, creative commons and other intellectual property rights in Artificial Intelligence
5. understand the future development of Artificial Intelligence
10. Course Outline
Week 1 History of AI
Week 2 How does AI change many industries?
Week 3 Intelligence Systems
Week 4 Types of AIs
Week 5 AI in Games
Week 6 Machine Learning Models
Week 7 Understand Neural Networks
Week 8 Building a Machine Learning Model
Week 9 Model Evaluation
Week 10 Real World AI and Impact on Society
Week 11 Future of AI, Threat and Dangers of AI
Week 12 Presentation
Week 13 Final Examination
11. Teaching Methods
Lecture, guided practice, group project
12. Teaching Media
Lecture/lab handouts, computer programs, online quizzes, Project Presentation
13. Measurement and Evaluation of Student Achievement
Student’s achievement will be evaluated according to the faculty and university standard, using the symbols: A, B, B+, C, C+, C, D+, D and F.
Weight:
1. Examination 30 %
2. Assignments 25 %
3. Presentation 25 %
4. Participation 20 %
Total 100 %
14. References
1. G.F. Luger, Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 6th edition. Addison-Wesley, 2009.
2. E. Rich & K. Knight, Artificial Intelligence. McGraw-Hill, 1991.
3. S. Russell & P. Norvig, Artificial Intelligence: A Modern Approach. Prentice Hall, 2006.
4. T. Mitchell, Machine Learning. McGraw-Hill, 1997.
15. Recommended textbooks and/or other documents/materials
- https://robocode.sourceforge.io/
- https://quickdraw.withgoogle.com/
- https://www.koshegio.com/decision-tree-classifier-calculator
- https://teachablemachine.withgoogle.com/
16. Instructors
Asst. Prof. Dr. Tanasanee Phienthrakul
Room: 6257, Building 3 (Red Building)
Department of Computer Engineering,
Faculty of Engineering, Mahidol University
17. Course Material:
https://classroom.google.com
Class code: yh4z446
** Use mahidol.edu account