Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Spring Semester
Year 2023 316889 Artificial Intelligence 1 3.0 3 ★ CHUI-YU CHIU GIEM
IMBA
GIFM
IMFI
CMGT 37 0 工管所、資財所、IMBA、IMFI和管理博合開

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2023	316889	Artificial Intelligence	1	3.0	3	★	CHUI-YU CHIU	GIEM IMBA GIFM IMFI CMGT	37	0	工管所、資財所、IMBA、IMFI和管理博合開

Course Plan：

Instructor CHUI-YU CHIU　Office Hours

Email cychiu@ntut.edu.tw

Last Updated 2023-01-08 00:20:50

Course Objective Artificial intelligence (AI) is a research field that studies how to realize the intelligent human behaviors on a computer. The ultimate goal of AI is to make a computer that can learn, plan, and solve problems autonomously. The main purpose of this course is to allow students understanding what the AI is, current theories and applications of AI. The main topics of this course include: Uninformed and informed Search Methods Local Search Methods Game Playing Neural Networks Reasoning under Uncertainty Artificial Intelligence applications in Engineering. Artificial Intelligence applications in Management.

Course Schedule ##Week 1-3 Introduction to Artificial Intelligence Uninformed Search Methods – searching a state space graph, problem representation in terms of states, goal test, operators, state-space graph search formulation, expanding a node, frontier list, partial solution path, solution path, search tree, breadth-first search, depth-first search, chronological backtracking, uniform-cost search, iterative-deepening search, bidirectional search, completeness, optimality, admissibility, time and space complexity, detecting repeated states, explored list. Informed Search Methods – Understand heuristic functions, evaluation functions, best-first search, greedy best-first search, beam search, algorithm A, algorithm A*, admissible heuristic. (paper discussion and representation of Artificial Intelligence applications in engineering and management) ##Week 4-7 Local Search Methods – Local search problem formulation, operators, neighborhood, move set, hill-climbing algorithm, local optima problem, hill-climbing with random restarts, stochastic hill-climbing (simulated annealing) algorithm, escaping local optima, Boltzman’s equation, cooling schedule, genetic algorithms, particle swarm optimization. Game Playing –game playing as search, search tree, branching factor, minimax algorithm, alpha-beta pruning algorithm, best case and worst case of alpha-beta vs. minimax, iterative-deepening with alpha-beta. (paper discussion and representation of Artificial Intelligence applications in engineering and management) ##Week 8-9 Mid-term report & presentation ##Week 10-12 Neural Networks –input units, output units, weight space, multi-layer feed-forward networks, hidden units, sigmoid function, back-propagation algorithm, deep learning. (paper discussion and representation of Artificial Intelligence applications in engineering and management) ##Week 13-15 Reasoning under Uncertainty – Random variable, conditional probability, posterior probability, product rule, chain rule, Bayes’s rule, conditionalized version of Bayes’s rule, naïve Bayes classifier, Bayesian networks. Fuzzy set theory, Fuzzy operations, Fuzzy control (paper discussion and representation of Artificial Intelligence applications in engineering and management) ##Week 16-18 Final project workshop and presentation

Evaluation and grading policy • Homeworks, paper study and discussion, class participation: 40% • Midterm report & presentation: 30% • Final project: 30%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:No
Reference textbook: Artificial Intelligence: A Modern Approach, Russell, Stuart J.; Norvig, Peter Artificial Intelligence: A Guide to Intelligent Systems, 3/e, Michael Negnevitsky Open access journal papers

The access to curricular consultation cychiu@ntut.edu.tw

The course corresponds to the SDGs

Does the course incorporate AI

Note
In case of distance learning, please find the information at https://oaa.ntut.edu.tw/p/404-1008-98622.php?Lang=zh-tw
Course information will be released on the NTUT i-school plus platform.

Instructor	CHUI-YU CHIU　Office Hours
Email	cychiu@ntut.edu.tw
Last Updated	2023-01-08 00:20:50
Course Objective	Artificial intelligence (AI) is a research field that studies how to realize the intelligent human behaviors on a computer. The ultimate goal of AI is to make a computer that can learn, plan, and solve problems autonomously. The main purpose of this course is to allow students understanding what the AI is, current theories and applications of AI. The main topics of this course include: Uninformed and informed Search Methods Local Search Methods Game Playing Neural Networks Reasoning under Uncertainty Artificial Intelligence applications in Engineering. Artificial Intelligence applications in Management.
Course Schedule	##Week 1-3 Introduction to Artificial Intelligence Uninformed Search Methods – searching a state space graph, problem representation in terms of states, goal test, operators, state-space graph search formulation, expanding a node, frontier list, partial solution path, solution path, search tree, breadth-first search, depth-first search, chronological backtracking, uniform-cost search, iterative-deepening search, bidirectional search, completeness, optimality, admissibility, time and space complexity, detecting repeated states, explored list. Informed Search Methods – Understand heuristic functions, evaluation functions, best-first search, greedy best-first search, beam search, algorithm A, algorithm A*, admissible heuristic. (paper discussion and representation of Artificial Intelligence applications in engineering and management) ##Week 4-7 Local Search Methods – Local search problem formulation, operators, neighborhood, move set, hill-climbing algorithm, local optima problem, hill-climbing with random restarts, stochastic hill-climbing (simulated annealing) algorithm, escaping local optima, Boltzman’s equation, cooling schedule, genetic algorithms, particle swarm optimization. Game Playing –game playing as search, search tree, branching factor, minimax algorithm, alpha-beta pruning algorithm, best case and worst case of alpha-beta vs. minimax, iterative-deepening with alpha-beta. (paper discussion and representation of Artificial Intelligence applications in engineering and management) ##Week 8-9 Mid-term report & presentation ##Week 10-12 Neural Networks –input units, output units, weight space, multi-layer feed-forward networks, hidden units, sigmoid function, back-propagation algorithm, deep learning. (paper discussion and representation of Artificial Intelligence applications in engineering and management) ##Week 13-15 Reasoning under Uncertainty – Random variable, conditional probability, posterior probability, product rule, chain rule, Bayes’s rule, conditionalized version of Bayes’s rule, naïve Bayes classifier, Bayesian networks. Fuzzy set theory, Fuzzy operations, Fuzzy control (paper discussion and representation of Artificial Intelligence applications in engineering and management) ##Week 16-18 Final project workshop and presentation
Evaluation and grading policy	• Homeworks, paper study and discussion, class participation: 40% • Midterm report & presentation: 30% • Final project: 30%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:No Reference textbook: Artificial Intelligence: A Modern Approach, Russell, Stuart J.; Norvig, Peter Artificial Intelligence: A Guide to Intelligent Systems, 3/e, Michael Negnevitsky Open access journal papers
The access to curricular consultation	cychiu@ntut.edu.tw
The course corresponds to the SDGs
Does the course incorporate AI
Note	In case of distance learning, please find the information at https://oaa.ntut.edu.tw/p/404-1008-98622.php?Lang=zh-tw Course information will be released on the NTUT i-school plus platform.