This course introduces fundamental concepts in AI using the Python programming language. Topics to be covered include Heuristic search, local search, optimization. Application to problems including General Problem Solving, Optimization, Search in Games, Propositional logic.
Office Hour: Monday 1:00-2:00pm 3C08B
Class meeting time: Monday/Wednsday 2:30-3:45pm 3C13
Lecture notes
Lecture_01_Course logistics (2024.1.8)
Lecture_02_Python&Jupyter Notebook (2024.1.10)
Lecture_03_Introduction (2024.1.15)
Lecture_04_Intelligent agents (2024.1.17)
Lecture_05_Agents lab (2024.1.22)
Lecture_06_Solving problems by searching (2024.1.24)
Lecture_07_Uninformed search (2024.1.29)
Lecture_08_Informed search (2024.1.31)
Lecture_09_Advanced topics in search (2024.2.5)
Lecture_10_search lab (2024.2.7)
Lecture_11_Search in complex environment (2024.2.12)
Lecture_12_Search in complex environment 2 (2024.2.14)
Lecture_13_Relax the assumptions of determinism and observability (2024.2.26)
Lecture_14_Complex environment lab (2024.3.4)
Lecture_15_Constraint Satisfaction Problems (CSP) (2024.3.6)
Lecture_16_CSP local consistency (2024.3.11)
Lecture_17_CSP backtracking (2024.3.13)
Lecture_18_Adversarial search and games (2024.3.18)
Lecture_19_α–β pruning and Monte Carlo Tree Search (MCTS) (2024.3.20)
Lecture_20_MCTS and stochastic games (2024.3.27)
Lecture_21_Logical agent (2023.4.1)
Lecture_22_Propositional logic (2023.4.3)
Exams
Midterm (2024.2.28)
Final (2024.4.30 MAN 2M77)
Assignments
Assignment 1 (Due at 1:00pm 2024.2.12)
Assignment 2 (Due at 1:00pm 2024.3.8)
Assignment 3 (Due at 1:00pm 2024.3.29)