[ml] Stanford Machine Learning Course CS229

[Brian Morris]

hey you guys don't know me I know, but

I looked at some of these materials before and watched one lecture and
looked through the book.

It seems to me to be a little out of date, it is aimed strictly at an
undergraduate, engineering audience not a scientific, research one.

The teacher looks to be an assistant professor looking for some way to
boost his tenure chances...



I signed up to this list a few months ago and felt rather intimidated
by the level that you were working at (like postdoc stuff or at least
thesis procrastinators) -- but now I think you are aiming too low for
yourselves...

All this could be my own prejudices though, I was looking for some
specific things which I didn't find much help with but also overall I
thought it was a little too ho-hum, really.

Brian

Yes I would like to participate possibly in your group if / when I can
get my schedule open, my math and cog sci / linguistics is pretty good
but my programming skills are really weak and I am trying to get some
improvement there but it is hard since I have not done any for some
time and it was in more straightforward physics-math stuff.

An interest I would like to learn more about and fun and interesting
if I could find out more is autonomous systems, self-organizing,
unsupervised learning (maybe now you can see why I don't like that
course -- its 90% supervised  / training stuff)




On 8/20/10, 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
>>
>