[ml] Stanford Machine Learning Course CS229
arbzed at gmail.com
Thu Aug 19 16:38:16 PDT 2010
On Thu, Aug 19, 2010 at 4:32 PM, Micah Pearlman <micahpearlman at gmail.com>wrote:
> I'd certainly be interested!
> Micah Pearlman
> (biz) (415) 373-6034
> (mob) (415) 637-6986
> micahpearlman at gmail.com
> On Thu, Aug 19, 2010 at 4:24 PM, Thomas Lotze <thomas.lotze at gmail.com>
> > Joe,
> > This sounds like a great idea. I'm definitely interested.
> > -Thomas
> > On Thu, Aug 19, 2010 at 3:36 PM, Joe Hale <joe at jjhale.com> wrote:
> >> Hi,
> >> I was wondering if anyone out there wanted to form a study group to
> >> work through the Stanford Machine learning course. The videos of the
> >> lectures are on iTunesU and all the handouts and problem sets are
> >> online.
> >> The course consists of 20 lectures which are 1h 15m long each. I've
> >> pasted the syllabus at the end. It seems like it would provide a
> >> really solid foundation for future ML projects at Noisebridge for
> >> those interested in getting into ML but who maybe didn't get round to
> >> studying it at school.
> >> I figure we'd watch lectures on our own time and get together to
> >> discuss them and the problem sets.
> >> Let me know if you'd be interested.
> >> - Joe Hale
> >> :::The course details:::
> >> Machine Learning CS229
> >> http://www.stanford.edu/class/cs229/
> >> Course Description
> >> This course provides a broad introduction to machine learning and
> >> statistical pattern recognition. Topics include: supervised learning
> >> (generative/discriminative learning, parametric/non-parametric
> >> learning, neural networks, support vector machines); unsupervised
> >> learning (clustering, dimensionality reduction, kernel methods);
> >> learning theory (bias/variance tradeoffs; VC theory; large margins);
> >> reinforcement learning and adaptive control. The course will also
> >> discuss recent applications of machine learning, such as to robotic
> >> control, data mining, autonomous navigation, bioinformatics, speech
> >> recognition, and text and web data processing.
> >> Prerequisites
> >> Students are expected to have the following background:
> >> Knowledge of basic computer science principles and skills, at a level
> >> sufficient to write a reasonably non-trivial computer program.
> >> Familiarity with the basic probability theory. (CS109 or Stat116 is
> >> sufficient but not necessary.)
> >> Familiarity with the basic linear algebra (any one of Math 51, Math
> >> 103, Math 113, or CS 205 would be much more than necessary.)
> >> Course Materials
> >> There is no required text for this course. Notes will be posted
> >> periodically on the course web site. The following books are
> >> recommended as optional reading:
> >> Syllabus
> >> Introduction (1 class)
> >> Basic concepts.
> >> Supervised learning. (7 classes)
> >> Supervised learning setup. LMS.
> >> Logistic regression. Perceptron. Exponential family.
> >> Generative learning algorithms. Gaussian discriminant analysis. Naive
> >> Bayes.
> >> Support vector machines.
> >> Model selection and feature selection.
> >> Ensemble methods: Bagging, boosting, ECOC.
> >> Evaluating and debugging learning algorithms.
> >> Learning theory. (3 classes)
> >> Bias/variance tradeoff. Union and Chernoff/Hoeffding bounds.
> >> VC dimension. Worst case (online) learning.
> >> Practical advice on how to use learning algorithms.
> >> Unsupervised learning. (5 classes)
> >> Clustering. K-means.
> >> EM. Mixture of Gaussians.
> >> Factor analysis.
> >> PCA. MDS. pPCA.
> >> Independent components analysis (ICA).
> >> Reinforcement learning and control. (4 classes)
> >> MDPs. Bellman equations.
> >> Value iteration and policy iteration.
> >> Linear quadratic regulation (LQR). LQG.
> >> Q-learning. Value function approximation.
> >> Policy search. Reinforce. POMDPs.
> >> _______________________________________________
> >> ml mailing list
> >> ml at lists.noisebridge.net
> >> https://www.noisebridge.net/mailman/listinfo/ml
> > _______________________________________________
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