[ml] Stanford Machine Learning Course CS229

Joe Hale joe at jjhale.com
Wed Aug 25 11:05:13 PDT 2010


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.
>>>> _______________________________________________
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>>>> https://www.noisebridge.net/mailman/listinfo/ml
>>>
>>>
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>>
>>
>>
>> --
>> Whatever you can do or imagine, begin it;
>> boldness has beauty, magic, and power in it.
>>
>> -- Goethe
>>
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