What are Probabilistic Graphical Models?
Uncertainty is unavoidable in real-world applications: we can almost never predict with certainty what will happen in the future, and even in the present and the past, many important aspects of the world are not observed with certainty. Probability theory gives us the basic foundation to model our beliefs about the different possible states of the world, and to update these beliefs as new evidence is obtained.
These beliefs can be combined with individual preferences to help guide our actions, and even in selecting which observations to make. While probability theory has existed since the 17th century, our ability to use it effectively on large problems involving many inter-related variables is fairly recent, and is due largely to the development of a framework known as Probabilistic Graphical Models (PGMs). Topics covered include: Introduction and Overview. These will be covered in the first two weeks of the online class.
Game Theory - January 2012. Human-Computer Interfaces - January 2012. Software Engineering for Software as a Service - January 2012. Natural Language Processing - January 2012. Computer Science 101 - January 2012. About the Course UPDATE: we're doing a live, updated MOOC of this course at stanford-online July-2014 (not this Coursera version).
See here: CS101 teaches the essential ideas of Computer Science for a zero-prior-experience audience. Computers can appear very complicated, but in reality, computers work within just a few, simple patterns. CS101 demystifies and brings those patterns to life, which is useful for anyone using computers today. In CS101, students play and experiment with short bits of "computer code" to bring to life to the power and limitations of computers. Here is another video Nick created for this class. Course Syllabus CS101 topics are covered with a mixture of video lecture and active lab work, all in the browser:
Machine Learning. Machine learning is the science of getting computers to act without being explicitly programmed.
In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems.
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Introduction to Databases. About The Course A bold experiment in distributed education, "Introduction to Databases" is being offered free and online to students worldwide, October 10 - December 12, 2011. Students have access to lecture videos, are given assignments and exams, receive regular feedback on progress, and participate in a discussion forum. Those who successfully complete the course will receive a statement of accomplishment. Taught by Professor Jennifer Widom, the curriculum draws from Stanford's popular Introduction to Databases course. A high speed internet connection is recommended as the course content is based on videos and online exercises. About The Instructor Professor Jennifer Widom is the Fletcher Jones Professor and Chair of the Computer Science Department at Stanford University.
Why Learn About Databases? Databases are incredibly prevalent -- they underlie technology used by most people every day if not every hour. Course Description. Intro to AI - Introduction to Artificial Intelligence - Oct-Dec 2011.