Looks like neither AI-class nor DB-class will be rerun, at least not yet; if you fancied the AI-class, take Machine Learning instead. Ng is a great teacher. Because the course involves extensive amounts of practical coding, it is actually both fun and fulfilling, which I’m afraid I cannot say of the AI-class  – which has been informative, and at times challenging, but for me there is less satisfaction in a set of quiz grades than there is in building something and watching it work.

Technical courses

Machine Learning (Andrew Ng) – will be rerun in more or less its current form
Probabilistic Graphical Models (Daphne Koller)- a logical successor for ml-class survivors
Natural Language Processing (Chris Manning and Dan Jurafsky)
Cryptography (Dan Boneh)
Game Theory (Matthew O. Jackson and Yoav Shoham)
Human-Computer Interaction (Scott Klemmer)
Design and Analysis of Algorithms I (Tim Roughgarden)
Computer Science 101 (Nick Parlante)  – The beginner’s guide to these strange things they call ‘computers’ and ‘code’
Software Engineering for Software as a Service (Armando Fox and David Patterson)
Computer security (Dan Boneh, John Mitchell and Dawn Song) – How to ‘design secure systems and write secure code’

Electrical Engineering
Information Theory (Tsachy Weissman) – ‘the science of operations on data such as compression, storage and communication’. Begins in March 2012.

Complex Systems
Model Thinking (Scott E. Page) – building models of complex systems.

Entrepreneurship
Technology Entrepreneurship (Chuck Easley) – ‘understand the formation and growth of high-impact start-ups in areas such as information, green/clean, medical and consumer technologies.’
The Lean Launchpad (Steve Blank) -  Business models, customer development, and starting up your startup.

Civil Engineering
Making Green Buildings (Martin Fischer) – how to manage sustainable building projects.

Medicine
Anatomy (Sakti Srivastava) – knee bone connected to the hip bone, etc.

Caveat emptor
As these courses are free online, I suppose that really ought to read caveat lector or caveat auditor or something, but you know what I mean. Here’s the warning: each of these courses are supposed to take over ten hours a week. Follow the Stanford AI-Class Decision Diagram with care and attention when deciding whether to enrol.

P.S.
If you’re not a computer science or mathematics graduate, you will probably need to work on your maths for many of these courses. The Khan academy have very useful course material for areas like basic probability, Bayes and linear algebra/matrices.

P.P.S.
If anybody wonders what an unspecified number of thousands of dedicated students attempting to finish a midterm exam before the deadline can do to a server, wonder no more:

Having seen it repeatedly whilst trying to fill in the midterm forms, today I see this message every time I close my eyes…

* As it happens, not all these classes are run by Stanford. Software Engineering for Software as a Service is a Berkeley course (although one of the instructors, Armando Fox, was previously employed at Stanford), Computer Security is a joint effort, and Model Thinking is taught by Scott Page of the University of Michigan.

@SebastianThrun: Who's up for a $2000 Stanford degree?

You might have seen the intense publicity received by Stanford’s current experiment: Ai-Class, not to mention the sibling efforts ML-Class and DB-Class. These were described to the public as beta-releases of a new kind of education, and have been made available for free, possibly a once-in-a-lifetime offer, possibly never to be repeated. Class began in mid-October, and it’s not clear whether these will run again in their current form.

I joined two classes; AI-Class (artificial intelligence, taught by Sebastian Thrun and Peter Norvig) and ML-Class (machine learning, taught by Andrew Ng). Given that the midterm exam happens next week, I won’t be sharing my grades, but I would like to write a little about accessing these courses on various platforms.

First, a confession: despite the fact that the AI-class draws extensively on material from Russell & Norvig’s ‘Artificial Intelligence: a modern approach’, and the fact that I would’ve liked to use this to check out some ebook reader platforms, I haven’t been able to do so. There are various reasons for that, but the most compelling is :

Oh well.

There were other problems, anyway; the price of Norvig’s other books suggest that I would not have been happy to pay the price for a Kindle copy. Keep in mind that the office wouldn’t be paying; this is something I’m doing in what I laughably refer to as ‘spare time’. Norvig’s cheapest available Kindle download, Case Studies in Common Lisp, costs £41.89. If AI:AMA cost anything like that, I’d have ended up checking out the second-hand market anyway – you can pick up a second-hand copy for between a fiver and a tenner. Even if I’d bought a paper copy new it may have been cheaper; e-books attract VAT.
This got the Kindle out of the running very quickly. The primary use it can be put to during the course is revision of notes from the ML-class, which conveniently includes revision slides/PDFs.

That left the Apple iPad and Motorola Xoom, which could not only view the PDFs, but also access the videos offered by each site. In the case of ML-Class, a download link was even provided for each video – perfect, I thought, I’ll download them and watch the videos in transit. One difficulty: the iPad seems to disapprove of the concept of downloading files. Safari will consent to send pdfs to iBooks, but as for storing videos for later review, the obvious solutions involve a laptop and iTunes. If you are not always online, the need for advance planning – the faff factor, if you like – increases rapidly. The determined can mitigate the problem via applications for the iPad such as the MyMedia download manager, but the app-centric viewpoint is frustrating. Stanford could solve this through iTunes U – but how many channels must a provider support?

The Xoom did not go to the same finishing school as the iPad, if it went to one at all. Unaware that saving files from the browser and displaying them in anything available is an uncouth habit, it simply does it. It also seems to have passed through its formative years without learning that arbitrary soft-resetting is rude, so it occasionally does that as well.

ML-Class makes extensive use of Octave, a free and fairly Matlab-compatible language and interpreter, giving weekly assignments. The idea of Octave on a mobile device is not as far-fetched as it sounds – Nokia N800/810 owners were able to use both Octave and Gnuplot. Similar software packages, such as Addi and Mathmatiz, are available for Android. In general these are works in progress. iPad owners with a desktop copy of Matlab can try connecting to it remotely via Matlab Mobile, a function that is available through unofficial apps on Android. The interface is not, however, optimised for the iPad, and as with the problem of watching videos in transit, those with limited network connectivity will find this an imperfect solution. Why no Octave clone on iOs? The App Store, the GPL, and extensible interpreters apparently don’t mix, although since Apple changed the language in their SDK, some of the issues mentioned have been resolved.

To conclude: the iPad is polished, but I found myself reaching for the (heavier, clunkier) Android device instead. The Xoom is indeed something of a brick, but the iPad seems to be designed for a world with uniformly excellent 3G coverage, in which nobody ever spends much time offline.