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Machine Learning Course by Tom Mitchell
- nicklaf 12y agoDoes anybody have URLs for the videos? Clicking on the video link for a lecture asks you to install Silverlight.
- avinassh 12y agoI am checking on my iPad, though I don't get prompts to install silver light, I get redirected to some page, it says some error with session and to contact support :-/
- robert_tweed 12y agoAs far as I can tell there are lots of tiny fragments each about 1-2 seconds long rather than one video file. It's actually not just a video, but a specially designed player that shows the lecture video and the slides in sync, along with a bunch of bookmarks so you can jump to specific slides. Try it in Chrome. At least on the Mac, there's a dedicated but cut-down version, which does not require SilverLight. It's based on FlowPlayer and it also seems to work with Flash disabled.
- rasz_pl 12y agosame on opera 15, but after a second video shows up just fine, silverlight (ahahaha lol) is used for something else (slides?) under the video Video is displayed using Flash Flowplayer, you can extract mp4 from the source for example first one is http://scs.hosted.panopto.com/Panopto/Podcast/Embed/257476ca-e66a-4cdb-9ffc-8abdd3129954.mp4 http://scs.hosted.panopto.com/Panopto/Podcast/Embed/257476ca...
- warrick 12y agohttp://pastebin.com/raw.php?i=hbDV5iiS http://pastebin.com/raw.php?i=hbDV5iiS
- nicklaf 12y agoThank you!
- phlyingpenguin 12y agoAnybody got a mirror of lecture #6? It seems to be dead on the server.
- shamskazi 12y agoThanks! Lecture 6 seems to be offline, unfortunately.
- robert_tweed 12y agoDoes anyone have the book? Having looked through the ToC on Amazon, there are a few topics that interest me and it seems to be more in-depth than these lectures. But as it is from 1997 (and doesn't appear to have been updated), I'm concerned it will be a bit out of date.
- jskonhovd 12y agoI used this book in my Machine Learning course last spring at Georgia Tech. I wouldn't consider it out of date. It is missing a few topics like SVMs that we covered, but otherwise it's a good introduction.
- akramhussein 12y agoI read this cover to cover for my ML course at Imperial College London in UK. While not an easy read, reviewing the same topics a few times did make you understand the fundamentals better. AbeBooks sometimes has it going for £20(~$30). The exercises were a bit tricky as often the answers weren't attainable by simply following the book and resulted in you needing to consult other material.
- avinassh 12y agowhat are pre-requisites to start with the book? Requires heavy math understanding of Linear Algebra etc?
- mjn 12y agoIt's still a good introduction to the principles: how problems like regression, classification, and reinforcement learning are defined; concepts like overfitting, bias-variance tradeoff, etc.; some general classes of algorithms and how to analyze them. The age mainly affects its usefulness as an off-the-shelf guide to applied ML, because some of the currently best performing general-purpose algorithms aren't mentioned [1]. It also spends quite a bit of time on algorithms now considered mainly of historical interest, like version spaces. So imo its main current usefulness is as a foundational text, which it's quite good for. It helps that it's also well written and understandable. [1] A recent empirical analysis found that random forests and support vector machines seem to perform most consistently well at classification tasks, neither of which are in this book. http://jmlr.org/papers/v15/delgado14a.html http://jmlr.org/papers/v15/delgado14a.html
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- dalanmiller 12y agoI am registered for this course and I am quite excited!
- shostack 12y agoCan anyone recommend a good intro in machine learning that does teaches the building blocks of the math side of things? It is fascinating as a topic, but there is such a large prerequisite learning curve that it seems out of reach for those not as strong in math. That might just be the reality of it, but hoping there might be a better introduction (even something super simple like a Codecademy equivalent).
- jliechti1 12y agoMy guess is that you won't find any course that explains all the prerequisite math. It's probably more useful to build a solid foundation in probability theory (and therefore calculus) before going on. For machine learning, a good place to start is Andrew Ng's course on Coursera: https://www.coursera.org/course/ml https://www.coursera.org/course/ml It's pretty light on math, while at the same time giving you experience in implementing and understanding these techniques. From there, I might recommend Learning from Data and the associated video lectures: https://work.caltech.edu/telecourse.html https://work.caltech.edu/telecourse.html It is a bit of a jump, but it is a great course in presenting the field of machine learning and explaining the mathematical and statistical underpinnings in a systematic way.
- jamra 12y agoI just finished the Coursera course by Andrew Ng. It was great. The only hand waving done with math was when calculus was necessary. You can take some extra time to do that work yourself if you like, but you will not be missing the underpinnings of why things work statistically. The introduction to neural networks what finally gave me that aha moment. It is a very self contained course that is quite easy to follow. You can skip the programming exercises if you don't have the time.
- crazypyro 12y agoFor anyone interested in more about the specific math of neural networks, http://www.iro.umontreal.ca/~bengioy/dlbook http://www.iro.umontreal.ca/~bengioy/dlbook has a couple good introductory chapters that give overviews of most of the necessary topics for NNs, but also provides additional resource suggestions if you need more in-depth info on a certain subject.
- jokoon 12y agoI have a "pre-existing working knowledge of probability, linear algebra, statistics and algorithms", but can't he be more precise ? I mean to what difficulty are those math used ?
- jacobolus 12y agoPresumably this means at the level of an introductory undergraduate-level course in each of those subjects (the kind of courses targeted at first or second year science/engineering undergraduates). You could try working through the material, and if anything is beyond you, it shouldn’t be not too hard to find the appropriate textbook and catch up.
- anhng 12y agoThis is a Phd-level course at CMU. Pretty heavy on math. You can just take a look at the material to know the difficulty. If you are not sure, you probably should try easier ones such as Andrew Ng's on Coursera.
- jskonhovd 12y agoI am assuming he means at a undergraduate level. If you need a probability and statistics book to refresh, I can recommend "All of Statistics" by Wasserman.
- nitishmd 12y agoGood to see cmu opening up to open learning!
- aaronaaa 12y agoAs a current student, unfortunately, this is a rare gem. CMU still fails to make any attempts at free courseware.
- aroman 12y agoAs another current student... you are very mistaken. See: http://oli.cmu.edu http://oli.cmu.edu
- aaronaaa 12y agoHowever, with the exception of one or two of those courses, those are nothing but introductory courses. And while this is great material to have access to, grants almost no access to the wealth of knowledge at CMU.