Stanford University is going beyond open courseware by offering open worldwide registration for engineering courses to anyone interested in taking, one, two, or all three of its free engineering courses this fall.  Those who register can watch live-streamed video lectures, take quizzes, do homework, and ask questions of each other.  Feedback from the professor is limited — professors aggregate questions and answer only the most popular ones. Although no grades or official certificates are awarded, all students receive personalized feedback from the instructors at the end of the semester. Registration is now open and classes start October 1oth.

According to MAKE, there are over 43,000 people already registered for the Artificial Intelligence class. Is open learning the wave of the future? With high-speed internet access the only requirement,open learning from prestigious universities like Stanford promises to level the playing field for the gifted poor around the world. How far will Stanford extend this program? Will entire degree requirements eventually be offered as open learning courses online? If so, Will grades and degrees still matter? Will employers hire workers based on completion of these courses? How can Stanford finance the open learning expansion so that is it sustainable?

The three courses that are available in the program are:

Introduction to Artificial Intelligence

Professor: Peter Norvig

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.

Artificial Intelligence is the science of making computer software that reasons about the world around it. Humanoid robots, Google Goggles, self-driving cars, even software that suggests music you might like to hear are all examples of AI.

Introduction to Databases

Professor: Professor Jennifer Widom

Professor Widom is the Fletcher Jones Professor and Chair of the Computer Science Department at Stanford University.

This course covers database design and the use of database management systems for applications. It includes extensive coverage of the relational model, relational algebra, and SQL. It also covers XML data including DTDs and XML Schema for validation, and the query and transformation languages XPath, XQuery, and XSLT. The course includes database design in UML, and relational design principles based on dependencies and normal forms. Many additional key database topics from the design and application-building perspective are also covered: indexes, views, transactions, authorization, integrity constraints, triggers, on-line analytical processing (OLAP), and emerging “NoSQL” systems.

Introduction to Machine Learning

Professor: Professor Andrew Ng

Professor Ng is Director of the Stanford Artificial Intelligence Lab, the main AI research organization at Stanford, with 20 professors and about 150 students/post docs.

This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). (iv) Reinforcement learning. The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.