Computer Science - UG3/G
Course Artificial Intelligence I
Term 2023H
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 2023年秋
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; 9-409.

Week Date Lecture Handouts
1 2023/09/04 绪论：人工智能的定义，人工智能的起源与发展；人工智能的流派，人工智能的研究方法，人工智能的应用
2 2023/09/11 知识表示与推理：一阶谓词逻辑表示与推理；产生式规则表示与推理
3 2023/09/18 知识表示与推理：结构化知识表示与推理；知识图谱
4 2023/09/25 搜索算法：状态图搜索；与或树搜索，博弈树搜索
5 2023/10/02 -
6 2023/10/09 智能计算：进化算法，遗传算法；粒子群优化算法，蚁群算法
7 2023/10/16 机器学习：发展与定义，原理与结构，分类；经典方法：决策树，朴素贝叶斯
8 2023/10/23 神经网络：神经元与神经网络，神经元与神经网络，卷积神经网络，循环神经网络；经典方法：SVM，AdaBoost，随机森林
9 2023/10/30 实验1
10 2023/11/06 实验2
11 2023/11/13 课程设计1
12 2023/11/20 课程设计2

Evaluation

Textbook

Not mandatory but recommended:

Resources

  1. http://aima.cs.berkeley.edu/↩︎