【Introduction】

This course introduces the theory and techniques of artificial intelligence (AI) from the views of conception, history, representative problem, principles, etc. We focus on how to implement some intelligent functions using a generic computer, including problem solving, knowledge, inference, and programming, uncertain knowledge, natural language processing, computer vision, robotics, machine learning, etc. Through teaching these contents, our purpose is to make students clearly grasp the framework of AI, basic principles, and main methods. They cover the problem formulation, proposed solution, approaches to thinking, AI field introduction, etc. On the other hand, we will provide the main theory and techniques to solving some representative AI problems.

If you have any problems, please contact me via E-mail: zlwang AT ustc DOT edu DOT cn or visit my homepage.

【Textbook】

We choose the book “Artificial Intelligence: A modern Approach” (AIMA, 3rd) as the textbook of this course because of its popularity around the world and the timely update to knowledge. It is the leading textbook in Artificial Intelligence, and used in over 1300 universities in over 110 countries.  In particular, it is used as Berkeley’s CS 188.

About details of this book, see Textbook homepage. In particular, we provide some useful links and appendix (in Chinese) as follows:

• A scanned copy of the book;
• Appendix A: Mathematical Background;
• Appendix B: Notes on Languages and Algorithms;
• Online codes in Github with different languages;
• Online course.

【Slides】

The following slides are associated with the above textbook. Each set of slides corresponds to a book chapter:

Part I  Artificial Intelligence
• Ch1: Introduction
• Ch2: Intelligent Agents

Part II  Problem Solving

• Ch3: Solving Problems by Searching
• Ch4: Beyond Classical Search
• Ch5: Adversarial Search
• Ch6: Constraint Satisfaction Problems

Part III  Knowledge and Reasoning

• Ch7: Logical Agents (pdf, exc)
• Ch8: First-Order Logic (pdf, exc)
• Ch9: Inference in First-Order Logic (pdf, exc)
• Ch10: Classical Planning
• Ch11: Planning and Acting in the Real World
• Ch12: Knowledge Representation

Part IV  Uncertain Knowledge and Reasoning

• Ch13: Quantifying Uncertainty (pdf, exc)
• Ch14: Probabilistic Reasoning(pdf-a, pdf-b, exc)
• Ch15: Probabilistic Reasoning over Time (pdf, exc)
• Ch16: Making Simple Decisions (pdf, exc)
• Ch17: Making Complex Decisions

Part V  Learning

• Ch18: Learning from Examples
• Ch19: Knowledge in Learning
• Ch20: Learning Probabilistic Models
• Ch21: Reinforcement Learning

Part VI Communicating, Perceiving, and Acting

• Ch22: Natural Language Processing
• Ch23: Natural Language for Communication
• Ch24: Perception
• Ch25: Robotics

Part VII Conclusions

• Ch26: Philosophical Foundations
• Ch27: AI: The Present and Future

If you find any mistake or have improvement advise, don’t hesitate to inform me via Email: zlwang AT ustc DOT edu DOT cn.