1. Course number and name

010153524-63 ปัญญาประดิษฐ์และการเรียนรู้ของเครื่องจักรเบื้องต้น (Introduction to Artificial Intelligence and Machine Learning)

2. Credits and contact hours

3(3-0-6)

3. Instructor’s or course coordinator’s name

Asst.Prof. Dr.Pisit Vanichchanan

4. Text book, title, author, and year

1. Roy D. Yates and David Goodman, Probability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers, 2nd Edition, John Wiley & Sons, Inc., 2005.
2. Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.

5. Specific course information

1. brief description of the content of the course (catalog description)
   Linear model for regression; linear model for classification; neural networks; kernel; support vector machine; K-means clustering; expectation maximization; principle component analysis; hidden Markov model; reinforcement learning; applications.
2. prerequisites or co-requisites
   010153002-63 Computer Programming
3. indicate whether a required, elective, or selected elective (as per Table 5-1) course in the program
   Required : This subject is a required course.

6. Specific goals for the course

1. specific outcomes of instruction (e.g. The student will be able to explain the significance of current research about a particular topic.)
   1. CLO1 Be able to identify, formulate, and solve problems in the area of probability theory.
   2. CLO2 Be able to apply linear regression to solve related problems.
   3. CLO3 Be able to apply linear classification to solve related problems.
   4. CLO4 Be able to classify data using K-means clustering.
   5. CLO5 Be able to apply support vector machine to solve related problems.
   6. CLO6 Be able to apply expectation maximization to solve related problems.
   7. CLO7 Be able to apply principal component analysis to solve related problems.
   8. CLO8 Be able to apply neural networks to solve related problems.
   9. CLO9 Be able to apply reinforcement learning to solve related problems.
2. explicitly indicate which of the student outcomes listed in Criterion 3 or any other outcomes are addressed by the course.

   ABET Student Outcome (SO) Listed in Criterion 3

   Course learning outcome (CLO)

   SO1 an ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.

   CLO1 Be able to identify, formulate, and solve problems in the area of probability theory. CLO2 Be able to apply linear regression to solve related problems. CLO3 Be able to apply linear classification to solve related problems. CLO4 Be able to classify data using K-means clustering. CLO5 Be able to apply support vector machine to solve related problems. CLO6 Be able to apply expectation maximization to solve related problems. CLO7 Be able to apply principal component analysis to solve related problems.

7. Brief list of topics to be covered

8. Course Assessment