The objective of this class is to teach you modern AI. You will learn about the basic techniques and tricks of the trade. We also aspire to excite you about the field of AI.
Some of the topics in Introduction to Artificial Intelligence will build on probability theory and linear algebra. You should have understanding of probability theory comparable to that at our ST101: Introduction to Statistics class
This class introduces students to the basics of Artificial Intelligence, which includes machine learning, probabilistic reasoning, robotics, and natural language processing.
Overview of AI
Statistics, Uncertainty, and Bayes networks
Logic and Planning
Markov Decision Processes and Reinforcement Learning
Hidden Markov Models and Filters
Adversarial and Advanced Planning
Image Processing and Computer Vision
Robotics and robot motion planning
Natural Language Processing and Information Retrieval
This class is self paced. You can begin whenever you like and then follow your own pace. It’s a good idea to set goals for yourself to make sure you stick with the course.
This class will always be available!
Take a look at the “Class Summary,” “What Should I Know,” and “What Will I Learn” sections above. If you want to know more, just enroll in the course and start exploring.
Yes! The point is for you to learn what YOU need (or want) to learn. If you already know something, feel free to skip ahead. If you ever find that you’re confused, you can always go back and watch something that you skipped.
It’s completely free! If you’re feeling generous, we would love to have you contribute your thoughts, questions, and answers to the course discussion forum.
Collaboration is a great way to learn. You should do it! The key is to use collaboration as a way to enhance learning, not as a way of sharing answers without understanding them.
Udacity classes are a little different from traditional courses. We intersperse our video segments with interactive questions. There are many reasons for including these questions: to get you thinking, to check your understanding, for fun, etc... But really, they are there to help you learn. They are NOT there to evaluate your intelligence, so try not to let them stress you out.
Learn actively! You will retain more of what you learn if you take notes, draw diagrams, make notecards, and actively try to make sense of the material.
Peter Norvig is Director of Research at Google Inc. He is also a Fellow of the American Association for Artificial Intelligence and the Association for Computing Machinery. Norvig is co-author of the popular textbook Artificial Intelligence: A Modern Approach. Prior to joining Google he was the head of the Computation Sciences Division at NASA Ames Research Center.
Sebastian Thrun is a Research Professor of Computer Science at Stanford University, a Google Fellow, a member of the National Academy of Engineering and the German Academy of Sciences. Thrun is best known for his research in robotics and machine learning, specifically his work with self-driving cars.