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Free online version of Stanford's Fall 2011 Intro to AI course (ai-class.com)
381 points by finin on July 30, 2011 | hide | past | web | favorite | 44 comments



I took a version of this class (also taught by Norvig and Thrun) last year, and I definitely found it very enjoyable. I ended up taking it after CS 229 (which covers the mathematical underpinnings of machine learning with some rigor), so I unfortunately couldn't evaluate how good of an introductory course to AI it would be (having covered a lot of the concepts prior), but even still it was a class I enjoyed. Of particular interest was hearing the instructors draw parallels to their work (particularly with Google and the DARPA challenge), which made a lot of the theoretical concepts much more tangible and helped me recognize their practical applications.

you can also see a prior version of the class here: http://www.stanford.edu/class/cs221/


How necessary did you find the textbook, AIMA?


Not very much - most of the lectures stood on their own as far as explaining the concepts, although the book was sometimes useful to consult with regard to the details of an algorithm, for example. Much of that information can be found elsewhere though, so I imagine that you could still get a lot out of this course without AIMA.


This reminds me of CS229, of which there's an online version: http://www.youtube.com/watch?v=UzxYlbK2c7E

It's considerable more focused in scope, presenting the mathematics behind some of the more popular algorithms extensively used in machine learning, which is a subset of artificial intelligence. The course starts off pretty slow, but quickly gains speed and momentum. By the end, you should be fairly comfortable with clustering and classification/regression, among other topics. The lecture notes are also fantastic.


Stanford CS majors who take the AI track are required to take both CS 229 and this course, CS 221, so it definitely presents useful materials and concepts.


Yes this one is quite comprehensible too. Thanks for the link. Any other links you may suggest?


Could anyone explain to me why the HN community seems to have a particular interest in AI compared to other more "academic" areas? I have seen quite some amount of AI resources here, but at the same time for most startups discussed here they aren't particularly related to AI. Just curious.


I'm going to guess that you might be surprised at the number of startups that actually do rely on some aspect of AI, if you were to dig deep enough. Even if they're not "an AI startup" lots of companies these days are using some sort of machine learning, data mining, collaborative filtering, etc. stuff, which are aspects of AI. Look at any company that's using a recommender system of some sort... that stuff is one element of AI.

Aside from that, I think it's just that hackers have always been fascinated with AI. And lot of what we know as "hacker culture" to this day, dates back to people and events that happened in the AI research group at MIT, decades ago[1].

[1]: http://en.wikipedia.org/wiki/Hacker_(programmer_subculture)#...


My interest in AI is purely practical. The fist reason (very generic) is that I am (nearly) a lone contributor on a project that grew large. I have been using every trick in the book to make my code more abstract and manageable by one person. The next step in code reduction requires declarative programming (aka Prolog, though doesn't have to be) which requires deductive reasoning, which is a major topic of AI.

The second reason (more specific) is that I need to use an ontology (stored as a database) which is basically another topic of AI.


One possible explanation is in the syllabus:

AI has emerged as one of the most impactful disciplines in science and technology. Google, for example, is massively run on AI.


Google is massively run on databases, but that topic isn't as popular in media (or on HN). I think a big part is that it is such a highly visible topic that is also very romanticized, partially because its a field where serious inroads have been made (at least compared to expectations and last generations science fiction)


Databases are very "old world". Very much part of modern enterprise. Case in point, Oracle. As a result, it's not very exciting to people.

I expect that when there is an Oracle of AI, it too will lose its lustre.


Machine learning in particular is hugely important for startups--for example, given a rich training dataset of deals you've bought from a deal site (with predictors such as type of deal, location, price, datetime, number of friends who've bought them, etc), can we train a model that'll rank deals by likelihood that'll you'll purchase them?


Many technologists make money when they automate tedious processes executed by humans.


The course has the requirement: "A solid understanding of probability and linear algebra will be required."

Can anybody advise some books or online resources that one can use for 2 month to prepare himself for the course? Thanks.


Khan Academy has actually covered everything I've gone through in my statistics class and linear algebra classes, so I'll recommend that as I'm going two semesters with AIMA the upcoming year.

http://www.khanacademy.org/#linear-algebra

http://www.khanacademy.org/#probability

http://www.khanacademy.org/#statistics


These lectures on linear algebra by Prof. Gilbert Strang are an excellent resource: http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-...


Absolutely some of the best lectures I've ever seen, on the internet or otherwise. Can't recommend Prof. Strang enough.


Any recommendations for a probability course materials comparable in quality to Strang's?


can someone shortlist topics within linear algebra, probability and statistics that are more important for this AI call ? (Strang's class has 34 lectures and numerous topics in khan academy too)


Looks like a good way to sell more copies of AIMA[1]. (Don't get me wrong, I'm a fan, with my own signed copy--but not necessarily enough of a fan to "upgrade" to the third edition.)

[1] http://aima.cs.berkeley.edu/


Either that or there is a shortage of available AI engineers, so they are using this to identify and cull potentially good people that for one reason or another cannot or have not attended a Stanford caliber school.


Makes me think what would happen if someone excels in this course but is primarily from a college in third world country. Would they be tempted to call him/her to stanford? That would be very exciting if that happens.


Is this book required to take the course online?


Ah, my bad. I totally see I needed to scroll down >.< In case anyone is wondering, the page says "Access to a copy of Artificial Intelligence: A Modern Approach is also suggested."


This could be risky for Stanford. What happens when all the online students ace the class (demonstrating the curve was very easy), or get consistently better grades than the students enrolled at Stanford?


Chalk it up to self-selection bias, then optionally recruit them. Not a problem.


Personally, I would love to be recruited by Stanford to study CS. I have a Master's of Science in Financial Engineering, but I have always felt that I probably should have picked up a more solid CS background on my way to that degree. Also, it would be nice to meet some people face-to-face that have an interest in launching a start-up in the risk management arena. There is simply no interest for anything like that in the Cleveland area.

EDIT: I work in an interest rate risk management group for a major US bank. I can attest that there is definitely more room for good software in this space.


Or rather, a good problem to have.


This is a possibility, but if it does happen, I don't think it would reflect anything other than very determined people took the class that very possibly could have officially enrolled if they applied.


this is also a very introductory course. i think it's great for whetting the appetite for further research into particular areas, but it definitely glosses over a lot of the mathematical foundations that a course like CS 229 (mentioned elsewhere) thoroughly explores. as such, i'm not sure if there's much a top grade in this course would even signify beyond some amount of commitment and interest in artificial intelligence.


I may be straying a little off topic but it's worth noting that many of the mentioned course material and much more from a wide variety of subjects can be found at http://www.academicearth.org/.

This place is filled with awesome. Maybe you know?


Thanks for sharing. Very useful.


I find this approach interesting compared something like OpenCourseware. If I understand correctly, this is a very similar approach, with a graded portion not typically offered at something like Khan or Open Courseware?

I'm curious about any enrolment caps and what's next for this approach.

EDIT: missing word.


Could someone describe what sort of prerequisite knowledge is required for following along a course like this?


The syllabus says "A solid understanding of probability and linear algebra will be required." -- I imagine programming experience and knowledge of basic algorithms and data structures would be helpful as well.


Very cool. I'll be taking the intro AI course at my school this semester, so it will be fun to compare. Definitely excited to hear what Thrun and Norvig have to say. I met a colleague of Thrun's in Tokyo earlier this summer. By far the most interesting conversation of the entire trip.


That is really cool. I've forwarded the link to a couple instructors in the CS dept at my school. It seems like a great chance for some of us in the frozen northern wastelands of Vancouver to see how we stack up against the best and brightest in the US.


I decided to sign up for the course (I don't think I can officially sign up until later this Summer) but I'm not sure that I'll have the time or that I can afford the text. I'm also a Sophomore CS student, so the work would be very challenging for me.


I wish they had a version of a class like this that was meant to fit more easily into spare time. I'll be taking 18 hours at my own university, and I don't think I'll be able to set aside enough time to put a serious effort into this class.


You should definitely have more than enough time if you are interested in it. I have about 35 hours worth of class including labs in the Fall and I still plan to find time for this course.


I'm considering trying to take this and get my university to give me credit for it (it's Stanford, from one of the guys that wrote the textbook and I am getting a grade). However, I'm thinking whatever this class costs may hold me back.


That's awesome. I wonder if I can convince my college to count it for credit.


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