教學大綱與進度

課程基本資料：

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
110-2	302631	人工智慧	1	3.0	3	★	邱垂昱	工管所 管理外國學生專班 資財所 國際金融科技專班 管理博所	36	2	IMBA&IMFI&工管所&管理博班&資財所

教學大綱與進度：

教師姓名	邱垂昱
Email	cychiu@ntut.edu.tw
最後更新時間	2022-02-18 17:58:16
課程大綱	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.
課程進度	##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
評量方式與標準	• Homeworks, paper study and discussion, class participation: 40% • Midterm report & presentation: 30% • Final project: 30%
使用教材、參考書目或其他	【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】 使用外文原文書：否 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
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課程對應SDGs指標
備註	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.