NCHU Course Outline
Course Name (中) 人工智慧概論(6743)
(Eng.) Introduction to Artificial Intelligence
Offering Dept Intelligence Science, Engineering and Technology Master Degree Program
Course Type Required Credits 3 Teacher CHING TAK SHING
Department Intelligence Science, Engineering and Technology Master Degree Program/Graduate Language English Semester 2024-FALL
Course Description The basic concepts of AI/machine learning algorithms will be introduced. Also, the students will understand how to implement those methods to solve the problems occurred in our daily life. More importantly, student will learn how to apply AI/machine learning in scientific, technical and business fields.
Prerequisites
self-directed learning in the course Y
Relevance of Course Objectives and Core Learning Outcomes(%) Teaching and Assessment Methods for Course Objectives
Course Objectives Competency Indicators Ratio(%) Teaching Methods Assessment Methods

Discussion
Lecturing
Attendance
Assignment
Quiz
Course Content and Homework/Schedule/Tests Schedule
Week Course Content
Week 1 Introduction: What is this class about? What is AI?
”Machine Learning:
Linear predictors,
Loss minimization,
Stochastic gradient descent”
Week 2 ”Machine Learning:
Features, neural networks,
Gradients without tears,
Nearest Neighbors”
Week 3 ”Machine Learning:
Generalization,
Unsupervised learning”
Week 4 ”Search:
Tree search,
Dynamic programming,
Uniform cost search”
Week 5 ”Search:
Learning costs,
A* search,
Relaxation”
Week 6 ”Markov decision processes:
MDPs, Policy evaluation, value iteration”
Week 7 ”Markov decision processes:
Reinforcement learning,
Monte Carlo, SARSA, Q-learning,
Exploration/exploitation, function approximation”
Week 8 ”Game playing:
Minimax, expectimax,
Evaluation functions,
Alpha-beta pruning”
Week 9 ”Game playing:
TD learning,
Game theory”
Week 10 ”Constraint satisfaction problems:
Factor graphs,
Backtracking search,
Dynamic ordering, arc consistency”
Week 11 ”Constraint satisfaction problems:
Beam search, local search
Conditional independence, variable elimination”
Week 12 ”Bayesian network:
Probabilistic inference
Hidden Markov models”
Week 13 ”Bayesian network:
Forward-backward
Particle filtering
Gibbs sampling”
Week 14 ”Bayesian network:
Learning Bayesian networks
Laplace smoothing
Expectation Maximization”
Week 15 ”Logic:
Syntax versus semantics
Propositional logic
Horn clauses”
Week 16 ”Logic:
First-order logic
Resolution”
Week 17 Self-directed Learning (Video appreciation and discussion on topics related to AI)
Week 18 Self-directed Learning (Video appreciation and discussion on topics related to AI)
Evaluation
Attendance 10%, Assignment 60%, Quiz 30%,
Textbook & other References
(1) 張志勇. 人工智慧 三版. 全華圖書. 2023年. ISBN:9786263287037
(2) 蔡炎龍等. 少年Py的大冒險-成為Python AI深度學習達人的第一門課. 全華出版. 2022年. ISBN:9786263282964
(3) Philip C. Jackson Jr. Introduction to Artificial Intelligence. ISBN: 9780486248646
Teaching Aids & Teacher's Website
https://www.bme.nchu.edu.tw/members/tsching/
Office Hours
Monday 12:00~13:00
Sustainable Development Goals, SDGs
include experience courses:N
Please respect the intellectual property rights and use the materials legally.Please repsect gender equality.
Update Date, year/month/day:2024/08/19 09:50:10 Printed Date, year/month/day:2024 / 10 / 07
The second-hand book website:http://www.myub.com.tw/