[ml] Stanford Machine Learning Course CS229

Mike Schachter mike at mindmech.com
Wed Aug 25 12:23:19 PDT 2010


I gotta work late and won't be in, then will be away for next week. See
everyone in two weeks, good luck!

 mike


On Wed, Aug 25, 2010 at 11:05 AM, Joe Hale <joe at jjhale.com> wrote:

> Sounds good to me,
>
> See you this evening.
>
> Joe
>
> On 25 August 2010 09:55, Micah Pearlman <micahpearlman at gmail.com> wrote:
> > I'm up for it.
> >
> > Cheers, -Micah
> >
> > Micah Pearlman
> > (biz) (415) 373-6034
> > (mob) (415) 637-6986
> > micahpearlman at gmail.com
> >
> >
> >
> >
> > On Wed, Aug 25, 2010 at 9:34 AM, Glen Jarvis <glen at glenjarvis.com>
> wrote:
> >> So, we're still up for meeting this evening at 7:30 p.m....
> >> Reminder to all: that's tonight...
> >> So, we're discussing the Stanford Machine learning course recent thread?
> >>
> >> Cheers,
> >>
> >> Glen
> >> On Fri, Aug 20, 2010 at 6:43 PM, Mike Schachter <mike at mindmech.com>
> wrote:
> >>>
> >>> Hey Joe,
> >>>
> >>> I'm definitely up for something like this, but my schedule is a bit of
> >>> a mess for the next month or two. Why not come to next week's meetup
> >>> on Wednesday @ 7:30pm and we can all talk about it? It sounds like
> >>> a great idea!
> >>>
> >>>   mike
> >>>
> >>>
> >>> 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
> >>>
> >>>
> >>> _______________________________________________
> >>> ml mailing list
> >>> ml at lists.noisebridge.net
> >>> https://www.noisebridge.net/mailman/listinfo/ml
> >>>
> >>
> >>
> >>
> >> --
> >> Whatever you can do or imagine, begin it;
> >> boldness has beauty, magic, and power in it.
> >>
> >> -- Goethe
> >>
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> >>
> >>
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