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I'm enrolled in the online Applied ML class from Stanford, and I've also been watching this course from CMU (I'm up to the Graphical Model 4 lecture - almost th
by monk_the_dog 15y ago
I'm enrolled in the online Applied ML class from Stanford, and I've also been watching this course from CMU (I'm up to the Graphical Model 4 lecture - almost the midterm). If you've taken at least one stats class you'll get much more out of CMU's class.
BTW, here are some good online resources for machine learning:
* The Elements of Statistical Learning (free pdf book): http://www-stat.stanford.edu/~tibs/ElemStatLearn/ http://www-stat.stanford.edu/~tibs/ElemStatLearn/
* Information Theory, Inference, and Learning Algorithms (free pdf book): http://www.inference.phy.cam.ac.uk/mackay/itila/ http://www.inference.phy.cam.ac.uk/mackay/itila/
* Videos from Autumn School 2006: Machine Learning over Text and Images: http://videolectures.net/mlas06_pittsburgh/ http://videolectures.net/mlas06_pittsburgh/
* Bonus link. An Empirical Comparison of Supervised Learning Algorithms (pdf paper): http://www.cs.cornell.edu/~caruana/ctp/ct.papers/caruana.icml06.pdf http://www.cs.cornell.edu/~caruana/ctp/ct.papers/caruana.icm... (Note the top 3 are tree ensembles, then SVM, ANN, KNN. Yes, I know there is no 'best' classifier.)
- danso 15y agoI love it when people link to freely available academic texts, thank you. Here's another one from Stanford: Mining of Massive Datasets http://infolab.stanford.edu/~ullman/mmds.html http://infolab.stanford.edu/~ullman/mmds.html
- monk_the_dog 15y agoI just took a quick look on the chapter on clustering. Looks good! I'll put it on the ever growing stack. Thanks!
- bhickey 15y agoTo your list, I'd like to add Jaynes's 'Probability Theory' A few chapters are freely available here: www-stat.wharton.upenn.edu/~steele/Publications/PDF/PT.pdf (The publisher asked the book's editor to stop distributing the whole PDF.)
- zeratul 15y agoAbout the bonus link: It does not make sense to compare ensamble methods (bagging & boosting) with single instance classifiers. In practice, you try all classifiers and then you use best to create an ensamble. The paper leaves me unsatisfied, thinking that probably bagging or boosting SVM would give the best results.
- monk_the_dog 15y agoYou make a good point. Ensemble methods seem to outperform single classifiers. There's no reason you can't have an ensemble of SVMs. The paper should have included something other than an ensemble of trees. I tried to find a paper comparing an ensemble of SVM to an ensemble of trees and I came up empty (after a quick search). I did find papers showing ensembles of SVMs outperforming a single SVM. I also found a comment on a paper claiming an ensemble of trees out outperformed a "Parallel Mixture of SVM" (see here: http://www.mitpressjournals.org/doi/abs/10.1162/089976604323057416?journalCode=neco http://www.mitpressjournals.org/doi/abs/10.1162/089976604323...). Of course, that's not a great source. I absolutely agree they should have included ensembles other than trees. I don't necessarily agree an ensemble of SVM would have beat an ensemble of trees. It would have been interesting to see.
- zeratul 15y agoThere was a suicide note emotion classification challenge: http://computationalmedicine.org/home-0 http://computationalmedicine.org/home-0 Very noisy and sparse data. 25 teams. 22 system description papers. The winner used SVM ensamble.
- monk_the_dog 15y agoZeratul, you're obviously into ML. Would you mind if I asked what your application is? I'm just curious. I work in computer vision. When I do a machine learning problem, I spend most of my time brainstorming and implementing good features. I'm getting deeper into ML (and loving it). I'm always curious what other people are doing with ML.
- shriphani 15y agoI made the mistake of enrolling in a graduate level ML class without a strong foundation in statistics - my transcript is now going to be defaced permanently. But thanks for the inference text - is there an OCW version of an inference course?