[ml] Stanford Machine Learning Course CS229

Micah Pearlman micahpearlman at gmail.com
Wed Aug 25 09:55:04 PDT 2010


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
>>
>>
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>
>
> --
> Whatever you can do or imagine, begin it;
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>
> -- Goethe
>
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