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([MainPage|Back to homepage]) ==Information== * '''授课对象''': 计算机系本科生(一班) * '''教室''': 仙林校区仙2-104 * '''时间''': 周五5-6节 * '''教材''': Stuart J. Russell, Peter Norvig. Artificial Intelligence: A Modern Approach (3rd edition), Pearson, 2011. * '''助教''': [^http://lamda.nju.edu.cn/huyq|胡毅奇] * '''总评''': 课程作业 + 期末考试 * '''课程讨论QQ群''':513053106 * '''考试''': <font color="red">6月26日 16:30-18:30 仙1-319 </font> ==相关课程== 建议同时选修“[^http://cs.nju.edu.cn/zhouzh/zhouzh.files/course/ml.htm|机器学习]”、“[^http://cs.nju.edu.cn/lim/courses/IntroDM/IntroDM.htm|数据挖掘]”、“[^http://cs.nju.edu.cn/_upload/tpl/00/ed/237/template237/teaching_PR.html|模式识别]”课 ==作业== 本次课程有四次作业,将基于GVGAI框架,请立即开始熟悉该框架:[http://www.gvgai.net]<br/> * [course_ai17_hw1|>>>作业1: Bait游戏 - 关于搜索>>>] <s>截止日期: 3月18日 20:00</s> * [course_ai17_hw2|>>>作业2: 黑白棋游戏 - 关于博弈>>>] <s>截止日期: 4月8日 20:00</s> * [course_ai17_hw3|>>>作业3: Aliens游戏 - 关于监督学习>>>] <s>截止日期: 4月29日 20:00</s> * [course_ai17_hw4|>>>作业4: PacMan游戏 - 关于强化学习>>>] <font color="red">截止日期: 7月10日 20:00</font> ==课程材料== # Introduction ([{UP}course_ai17/Lecture1.pdf|Download PDF]) # Search 1: Uninformed Search ([{UP}course_ai17/Lecture2.pdf|Download PDF]) # Search 2: Informed Search ([{UP}course_ai17/Lecture3.pdf|Download PDF]) # Search 3: Adversarial Search ([{UP}course_ai17/Lecture4.pdf|Download PDF]) # Search 4: Beyond Classical Search: Bandit, Monte-Carlo Tree Search, and General Search ([{UP}course_ai17/Lecture5.pdf|Download PDF]) # Search 5: Constraint Satisfaction Problems ([{UP}course_ai17/Lecture6.pdf|Download PDF]) # Knowledge 1: Propositional Logic ([{UP}course_ai17/Lecture7.pdf|Download PDF]) # Knowledge 2: First Order Logic ([{UP}course_ai17/Lecture8.pdf|Download PDF]) # Knowledge 3: SAT, Planning and Ontology ([{UP}course_ai17/Lecture9.pdf|Download PDF]) # Uncertainty 1: Probability & Bayesian Network ([{UP}course_ai17/Lecture10.pdf|Download PDF]) # Uncertainty 2: Inference in Bayesian Network ([{UP}course_ai17/Lecture11.pdf|Download PDF]) # Learning 1: Supervised Learning & Decision Trees ([{UP}course_ai17/Lecture12.pdf|Download PDF]) # Learning 2: Neural Networks ([{UP}course_ai17/Lecture13.pdf|Download PDF]) # Learning 3: Principles of Supervised Learning ([{UP}course_ai17/Lecture14.pdf|Download PDF]) # Learning 4: Linear Models ([{UP}course_ai17/Lecture15.pdf|Download PDF]) # Learning 5: Ensemble Learners ([{UP}course_ai17/Lecture16.pdf|Download PDF]) # Learning 6: Feature Processing ([{UP}course_ai17/Lecture17.pdf|Download PDF]) # Learning 7: Deep Learning ([{UP}course_ai17/Lecture18.pdf|Download PDF]) # Learning 8: Reinforcement learning ([{UP}course_ai17/Lecture19.pdf|Download PDF]) # Final: On Artificial Intelligence ([{UP}course_ai17/Final.pdf|Download PDF]) <small>slides are derived from Russell's in http://aima.cs.berkeley.edu/instructors.html</small> ==学术资源== * 人工智能领域学术期刊/杂志: ** [^http://dblp.uni-trier.de/db/journals/ai/|Artificial Intelligence] ** [^http://dblp.uni-trier.de/db/journals/jair/|Journal of Artificial Intelligence Research] ** [^http://dblp.uni-trier.de/db/journals/aim/|AI Magazine] ** [^http://dblp.uni-trier.de/db/journals/air/|Artificial Intelligence Review] ** [^http://dblp.uni-trier.de/db/journals/expert/|IEEE Intelligent Systems] ** [^http://tist.acm.org/index.php|ACM Transactions on Intelligent Systems and Technology] * 人工智能领域学术会议 ** [^http://dblp.uni-trier.de/db/conf/ijcai|IJCAI] ** [^http://dblp.uni-trier.de/db/conf/aaai|AAAI] ** [^http://dblp.uni-trier.de/db/conf/ecai|ECAI] ** [^http://dblp.uni-trier.de/db/conf/pricai|PRICAI] ** [^http://dblp.uni-trier.de/db/conf/ausai|Aus-AI]
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