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Stanford's online Machine Learning class now open for enrollment
- mattdeboard 15y agoJust registered for this and looking forward to getting rolling.
- Sargis 15y agoHow difficult would this be for someone with minimal linear algebra knowledge?
- dpeck 15y agoFrom the course page: No particular math background is assumed, and we will go over the math concepts you need in the first couple of weeks.
- Homunculiheaded 15y agoHowever pretty early in the lectures they jump right into taking partial derivatives of matrices for calculating gradient descent. I think by 'no particular math background' they mean 'assuming nothing more than single and basic multivariable calculus plus linear algebra'. If your linear algebra and/or multi-variable calculus are weak/fuzzy (as they are for me) I think those sections will take quite a while to really understand.
- edtechre 15y agoThe course has some introductory Linear Algebra videos posted. I don't think you'd be able to understand multiple regression without some basic knowledge of linear algebra.
- samuel 15y agoLook for yourself: http://openclassroom.stanford.edu/MainFolder/CoursePage.php?course=MachineLearning http://openclassroom.stanford.edu/MainFolder/CoursePage.php?... The reference course on Stanford is CS229A instead of CS229, which is much lighter on Math. I have watched the videos on Linear and Logistic regression and the hardest derivations are elided, while the emphasis is put on intuitions.
- deleted 15y ago[deleted]
- Tycho 15y agoAm I likely to learn anything of immediate practical benefit on this course? I'm not a computer scientist, just someone who writes lots of scripts to work with Unix/Excel/RDBMS/XML for a finance company. (not that this would discourage me from taking the course, I'm just curious whether it will give me some new tools on my swiss-army-knife of programming knowledge, for my day job)
- silentbicycle 15y agoFor a quick overview of some machine learning fundamentals, you could check out Hilary Mason's "An Introduction to Machine Learning with Web Data" (http://shop.oreilly.com/product/0636920017493.do http://shop.oreilly.com/product/0636920017493.do) videos. They cover classification & clustering, with theory and a quick intro to some relevant Python libraries. For your purposes, one potential use could be combing through lots of financial data for transactions that look unusual. (Another very common use for classification is spam filtering.)
- seancron 15y agoI'd also recommend Programming Collective Intelligence (http://shop.oreilly.com/product/9780596529321.do http://shop.oreilly.com/product/9780596529321.do) which is filled with lots of python code showing how different algorithms and techniques can be used.
- silentbicycle 15y agoI thought about suggesting that, but when I started working through it, I kept running into errors in the code and formulas. He explains the concepts well, though. _Artificial Intelligence: A Modern Approach_ by Russell & Norvig ("AIMA") is also quite good, though a bit more demanding of the reader.
- gms 15y agoIt won't.
- rcavezza 15y agoI have a business background and I taught myself to code after getting involved in startups. I'm in the middle of deciding whether it is worth it to go back to school and get a master's degree in computer science. I hope this course will help me make that decision.
- amirmc 15y agoJust as a reminder for folks, there's a spreadsheet of HN readers who are taking part in the classes at http://bit.ly/pLCRzg http://bit.ly/pLCRzg There are 120+ HNers on there so you might find folks nearby you'd like to get in touch with. Edit: Obviously, if you're doing any of the courses feel free to add your details too
- gnok 15y agoSomewhat disappointed after realizing that the videos are all in Flash. So I can't watch this on my iPad. Does anyone know if these exact videos are also available on iTunes U?
- bradly 15y agoHere are some download links: http://www.reddit.com/r/mlclass/comments/kgbsq/headsupagainnewest2011videosavailable_at/ http://www.reddit.com/r/mlclass/comments/kgbsq/headsupagainn...
- gvkalra 15y agoThe links on reddit are old (as clarified here: http://www.ml-class.org/course/qna/view?id=8 http://www.ml-class.org/course/qna/view?id=8) There is a work around to do this (am not sure how ethical this is). Since it works am sharing the same. 1. Disable Flash in your browser (preferably use FF) 2. View video in HTML 5 mode (ex. http://www.ml-class.org/course/video/html5embed?videoid=1 http://www.ml-class.org/course/video/html5embed?videoid=1) 3. Right click on the video frame and select "Show only this frame" 4. Right click on the frame and select "Download with DownThemAll" ... Required: DownThemAll FF addon ....
- vl 15y agoI wonder if taking both ai-class.org and ml-class.org is going to be too much load for a person who is working full-time and if they are going to cover a lot of the same topics or not? Does anyone have an insight?
- jzawodn 15y agoI wonder the same and am having a tough time deciding which to take (or both).
- vl 15y agoFor now I signed up for both: if it will consume too much time I can drop one of them later, keeping more interesting one.
- webspiderus 15y agofor what it's worth, if you take the ML class, you will learn most of the things you would learn in the AI class and more - although it does get a bit rigorous, and will take more time than the AI class would. from personal experience, I feel like Ng's class gave me a more thorough foundation in the math behind the concepts, and was more challenging to boot - so I'd recommend it if you're feeling up for it.
- plinkplonk 15y agoI was just informed that Stanford's online ML course is based on CS229A rather than CS 229. From the CS229A home page "This class' emphasis is on Applied Machine Learning. Concretely, we want to give you the practical skills needed to get learning algorithms to work. Compared to CS229 (Machine Learning), we cover fewer learning algorithms, and also spend less time on the math and theory of machine learning, but spend much more time on the pratical, hands-on skills (and "dirty tricks") for getting this stuff to work well on an application. More of the homeworks will also focus on giving you practice implementing, modifying and debugging learning algorithms, and less on the mathematical underpinnings of machine learning" the core idea seems to be that this is a simplified and less rigorous version of CS 229. I am not sure of the tradeoffs between acquiring the "dirty tricks" vs getting a solid grasp of the underlying math (which would presumably result from taking CS229) Can anyone at Stanford tell me about how much difference is there in the perceived relative toughness of CS229 vs CS 229A? Do CS students take 229A or is it more people from the industry and other (non CS/Math) branches? Any opinions greatly appreciated.
- younata 15y agoExcellent. It'll be interesting to see how this and the ai-class will affect my performance in my other classes.
- yeison 15y agoStanford rocks! The timing of this is so convenient. I started watching the 2009 lectures from Professor Ng's Machine Learning class just last week.
- Tombar 15y agoIt'll be cool if they provide a link to download video lectures, my internet connection fails randomly and its hard to watch the videos smoothly ;S
- knarf55 15y agoGreat news. Will be enrolling to check this out.
- wyclif 15y agoAnybody taking the databases class that starts on Oct. 10th?
- amirmc 15y agoSee the spreadsheet at http://bit.ly/pLCRzg http://bit.ly/pLCRzg
- HSO 15y agoPerhaps this is the place and occasion to ask this question: What's the advantage of taking this class over reading a good (text)book incl. exercises contained therein? I realize people have different learning styles and I'm just not the type who learns well from listening. Still, even though I hated going to classes in school and university, I'm tempted by these offerings (AI, ML, DB). Perhaps I'm just looking for an excuse. So, anyone here who can give some good reasons for why taking this course is better than (or adds to) a "mere" book?
- deleted 15y ago[deleted]
- pakitan 15y agoI, too, am of the the type that learns better by reading, rather than listening but this format provides some additional "incentives" for the hardcore procrastinator who plans to read that AI textbook "later". Most important of all, if you want to complete the course, you're forced to follow a schedule and there are no excuses. Then there is the "reward" factor - you'll be getting a certificate and also all students are ranked based on their exam scores = you'll be motivated to work harder. Last but not least, the instructors will answer student questions about the course material. That's not something you can get from a book. These are the reasons why I signed up.
- stonemetal 15y agoI can't think of many hard benefits, but several soft benefits. Being able to ask questions is the only hard benefit I can think of. Deadlines and expectations might help motivate rather than that book siting on the shelf. You get a fancy piece of paper that lists your "grade" when you are done(achievement might motivate some). I have to admit the main draw (over a book) for me is curiosity about one of the nation's best CS programs and how it compares to the mid-west commuter college I went to.
- Tichy 15y agoAny ideas what the actual schedule will be? Like specific times when new videos go online, how much time will there be to hand in each exercise? I'd like to plan my time... Same questions for AI-Class.
- Benjo 15y agoThere's an unanswered question on this in the class's internal forum: http://www.ml-class.org/course/qna/view?id=17 http://www.ml-class.org/course/qna/view?id=17