ICGN134 - Introduction to Artificial Intelligence

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