Computer Science - UG3/G
Course Artificial Intelligence I
Term 2025 Spring
Final Exam
Credits 3
Staff WU Xiaokun 吴晓堃
Lecture 48 hours (12 weeks)
计算机科学专业核心课
课程名称 人工智能I
授课时间 2025年春
考试形式 考试
学分 3
讲者 吴晓堃，及其他
总计时长 48学时（12周）

Course Information

Short Intro

The objective of this course is to provide a complete introduction to Artificial Intelligence.

Description

Technically, there are two general forms of inference to deal with AI problems: logical and probabilistic inference. Agents in the real world need to handle partially known and uncertain worlds, which is best modeled by probabilistic approaches. So modern treatments are primarily based on statistics, and our discussions will also focus on that route.

Teaching Plan

This course is organized into a 12-week session (4 hours per week). The main contents are listed below:

本课程计划安排12周内容，主要讲授内容分别如下：

  1. 导言；智能体 Intro to AI, Rational Agents, Ch. 1, 2; State Spaces, Uninformed Search, Ch. 3.1–3.4
  2. 搜索；复杂环境 Informed Search: A* and Heuristics, Ch. 3.5–3.6; Local Search Ch. 4.1–4.2
  3. 命题逻辑与规划 Propositional Logic and Planning, Ch. 7.1–7.4; Boolean Satisfiability, DPLL, Ch. 8.1–8.2
  4. CSP, Ch. 5.1; Ch. 5.2–5.5
  5. 博弈论 Games: Trees, Minimax, Pruning, Ch. 6.1–6.3; Games: Expectimax, Monte Carlo Tree Search, Ch. 6.4–6.5
  6. Markov Decision Processes: Intro, Ch. 17.1; MDPs: Dynamic Programming, Ch. 17.2
  7. Reinforcement Learning, Ch 22.1–22.6; Ch 22.1–22.6
  8. Probability Review, Bayesian Networks: Ch. 13.1; Bayes Nets: Syntax and Semantics: Ch. 13.2
  9. Bayes Nets: Variable Elimination: Ch. 13.3; Bayes Nets: Sampling: Ch. 13.4
  10. Markov Chains, HMMs, filtering, Viterbi: Ch. 14.1–14.5; Dynamic Bayes Nets, Particle Filtering: Ch. 16.1–16.3
  11. Utility Theory, Rationality, Decisions: Ch. 16.1–16.3; Decision Networks and VPI: Ch. 16.5–16.7

Schedule

Monday, Wednesday; 9-405.

Week Date Lecture Handouts
1 2025/02/17 绪论：人工智能的基本概念，人工智能的发展简史；人工智能的流派，人工智能研究的基本方法方法
1 2025/02/19 绪论：人工智能的主要研究领域，人工智能的研究途径与方法
2 2025/02/24 搜索：基本搜索技术
2 2025/02/26 搜索：启发式搜索技术；实验环境配置
3 2025/03/03 搜索：博弈树搜索技术（Minimax、\alpha-\beta）
3 2025/03/05 实验：GoogLeNet
4 2025/03/10 搜索：博弈树搜索技术（Monte Carlo 树搜索）
4 2025/03/12 实验：DenseNet
5 2025/03/17 进化算法概述；遗传算法：要素建模
5 2025/03/19 实验：DenseNet
6 2025/03/24 遗传算法：举例
6 2025/03/26 实验：EfficientNet
7 2025/03/31 粒子群算法，蚁群算法
7 2025/04/02 实验：EfficientNet
8 2025/04/07 约束满足问题（CSP）
8 2025/04/09 实验：Transformer
9 2025/04/14 约束满足问题（CSP）
9 2025/04/16 实验：Transformer
10 2025/04/21 命题逻辑
10 2025/04/23 实验：Yolo
10 2025/04/27 一阶逻辑
11 2025/04/28 强化学习
11 2025/04/30 实验：分割网络
12 2025/05/07 总结与测试

Evaluation

Textbook

Not mandatory but recommended:

Resources

  1. http://aima.cs.berkeley.edu/↩︎