Course Syllabus

Course Information：

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

Spring Semester
Year 2026 359540 Artificial Intelligence 1 3.0 3 ★ Shing -chern You GCSIE 9 0 　

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2026	359540	Artificial Intelligence	1	3.0	3	★	Shing -chern You	GCSIE	9	0

Course Plan：

Instructor Shing -chern You

Email scyou@ntut.edu.tw

Last Updated 2026-02-06 19:15:33

Course Objective 人類在從事數學演算、推理交談、觀察等各類動作時，均展現出 "智慧"的現象。人工智慧即是利用電腦結合基本理論在有限度的範圍內從事上述各項工作，其中涵蓋了－自然語言了解、機器人學、電腦視覺、類神經網路、專家系統、自動化定理證明等，而本課程則主要在於提供必要之基礎，主要內容包括：1.搜尋法則，2. 命題與敘述邏輯，3. 知識系統與知識表達， 4. 馬可夫鍊及其應用，5.最佳化技術，6. 機器學習， 7.增強式學習，8. 其他相關技術。 Artificial intelligence seeks to introduce the elements of intelligence into programs and systems in application domain. Topics will include 1. Searching methods 2. Propositional and Predicate logic 3. Knowledge-based systems and representation of knowledge 4. Markov chain and its applications, 5. Optimization techniques, 6.Machine learning, 7. Reinforcement learning, 8. Related topics.

Course Schedule Week 1 Class announcement Introduction to artificial intelligence Week 2 Knowledge representation Semantic network Propositional logic & first-order logic Expert system Frames Problem solving & search BFS, DFS, UCS Week 3 Problem solving & search (Cont’d) Heuristics Search and A* Game trees Minimax α-β pruning Dynamic programming Week 4 Constraint satisfaction problems (CSP) Overview of optimalization Continuous & discrete functions Week 5: Continuous optimalization Gradient descent Lagrange multiplier Discrete functions Linear programming and interior-point method Branch-and-bound method Dynamic programming (again) Week 6 Metaheuristic optimization algorithms Genetic algorithm Simulated annealing Others Intro to probability Week 7 Markov chain Monte Carlo (MC) methods Computing pi via MC Week 8 MCMC (Markov Chain Monte Carlo) & Metropolis–Hastings algorithm Computing pi via MCMC Gibbs sampling Application in topic modeling (LDA) with Gibbs sampling Bayesian Networks & inference Week 9 MT Week 10 MT sol Hidden Markov chain (HMM) Week 11 HMM (cont’d) Application of HMM Week 12 Quick review of feedforward neural networks CNN and deep learning Week 13 Markov decision process (MDP) Introduction to reinforcement learning Policy function and value function Week 14 Introduction to reinforcement learning (Cont’d) Q learning DQN Policy-based RL REINFORCE Week 15 Policy-based RL (Cont’d) A3C PPO Week 16 Spare time if progress lags Additional topics (if time permits) Monte Carlo Tree Search (MCTS) Large-language models (LLMs) Week 17 Final exam Week 18 Presentation (Morning 10 -12, Internet)

Evaluation and grading policy MT 50 % Final 50 % Term Project (Bonus) 10 %

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:No
Lecture noted available.

The access to curricular consultation Contact me with
Email: scyou@mail.ntut.edu.tw
to arrange time for consulting.

Expanding teaching and resources

The course corresponds to the SDGs 無（None）

Does the course incorporate AI ● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）
● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）

Note
Exam time is 3 hours
Exam dates
MT: 4/21
Final 6/16
Project presentation: 6/23 online using re Teams

Instructor	Shing -chern You
Email	scyou@ntut.edu.tw
Last Updated	2026-02-06 19:15:33
Course Objective	人類在從事數學演算、推理交談、觀察等各類動作時，均展現出 "智慧"的現象。人工智慧即是利用電腦結合基本理論在有限度的範圍內從事上述各項工作，其中涵蓋了－自然語言了解、機器人學、電腦視覺、類神經網路、專家系統、自動化定理證明等，而本課程則主要在於提供必要之基礎，主要內容包括：1.搜尋法則，2. 命題與敘述邏輯，3. 知識系統與知識表達， 4. 馬可夫鍊及其應用，5.最佳化技術，6. 機器學習， 7.增強式學習，8. 其他相關技術。 Artificial intelligence seeks to introduce the elements of intelligence into programs and systems in application domain. Topics will include 1. Searching methods 2. Propositional and Predicate logic 3. Knowledge-based systems and representation of knowledge 4. Markov chain and its applications, 5. Optimization techniques, 6.Machine learning, 7. Reinforcement learning, 8. Related topics.
Course Schedule	Week 1 Class announcement Introduction to artificial intelligence Week 2 Knowledge representation Semantic network Propositional logic & first-order logic Expert system Frames Problem solving & search BFS, DFS, UCS Week 3 Problem solving & search (Cont’d) Heuristics Search and A* Game trees Minimax α-β pruning Dynamic programming Week 4 Constraint satisfaction problems (CSP) Overview of optimalization Continuous & discrete functions Week 5: Continuous optimalization Gradient descent Lagrange multiplier Discrete functions Linear programming and interior-point method Branch-and-bound method Dynamic programming (again) Week 6 Metaheuristic optimization algorithms Genetic algorithm Simulated annealing Others Intro to probability Week 7 Markov chain Monte Carlo (MC) methods Computing pi via MC Week 8 MCMC (Markov Chain Monte Carlo) & Metropolis–Hastings algorithm Computing pi via MCMC Gibbs sampling Application in topic modeling (LDA) with Gibbs sampling Bayesian Networks & inference Week 9 MT Week 10 MT sol Hidden Markov chain (HMM) Week 11 HMM (cont’d) Application of HMM Week 12 Quick review of feedforward neural networks CNN and deep learning Week 13 Markov decision process (MDP) Introduction to reinforcement learning Policy function and value function Week 14 Introduction to reinforcement learning (Cont’d) Q learning DQN Policy-based RL REINFORCE Week 15 Policy-based RL (Cont’d) A3C PPO Week 16 Spare time if progress lags Additional topics (if time permits) Monte Carlo Tree Search (MCTS) Large-language models (LLMs) Week 17 Final exam Week 18 Presentation (Morning 10 -12, Internet)
Evaluation and grading policy	MT 50 % Final 50 % Term Project (Bonus) 10 %
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:No Lecture noted available.
The access to curricular consultation	Contact me with Email: scyou@mail.ntut.edu.tw to arrange time for consulting.
Expanding teaching and resources
The course corresponds to the SDGs	無（None）
Does the course incorporate AI	● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools） ● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）
Note	Exam time is 3 hours Exam dates MT: 4/21 Final 6/16 Project presentation: 6/23 online using re Teams