[ml] Stanford Machine Learning Course CS229

Glen Jarvis glen at glenjarvis.com
Fri Aug 20 16:51:58 PDT 2010


I want to encourage you to continue with this. We'll run into problems.
They'll be frustrating. But, we know about them and it's so much better to
study as a group. There's motivation as a collective that isn't always there
by oneself.

Let me know how I can help... You know my only disclaimer where my boss's
class comes first (and will probably suck out all of my free time for the
semester).

Let's do it :)

Cheers,


Glen

On Fri, Aug 20, 2010 at 4:43 PM, Joe Hale <joe at jjhale.com> wrote:

> Hi,
>
> It seems like there is interest in forming a study group. We just need
> to figure out how to organise it now. Maybe we could talk about the
> best way to do this next wednesday. Glen makes some good points about
> difficulties we may encounter. There is a lot of material to get
> through and I appreciate that it needs to be fitted in around people's
> jobs and lives.
>
> I'd like to emphasise that there would not be anyone teaching the
> course beyond the professor in the videos. We'd just be working on the
> problem sets together and asking each other for clarification on
> points in the lectures. Hopefully newcomers could catch up by watching
> more lectures, and any questions they had would be useful revision for
> those who started earlier.
>
> I got an email from the maker of CoClass.com suggesting that his site
> could be useful for administrating the course.
>
> Have a good weekend,
>
> Joe
>
>
>
>
> On 19 August 2010 16:39, Glen Jarvis <glen at glenjarvis.com> wrote:
> > I'm obviously incredibly interested too. However, here are a few words of
> > caution:
> > * I have another course starting in a few weeks, taught by my boss, that
> I'm
> > required to take (and obviously need to do incredibly well on -- it has
> > priority and I'm already a very busy beaver),
> > * Although it may not be true for the ML group, I found when I taught a
> > Linux Certification Course at Noisebridge people were interested (very
> > interested) for about two weeks. They weren't generally willing to put
> the
> > homework to get a better understanding for the next week. But, we had a
> new
> > starter every week who was very *very* eager and I didn't want to leave
> > behind.. So, we were constantly in the beginning mode and either loosing
> > people by progressing too far, or boring people catering to the new
> people.
> > Just a few thoughts in case we hadn't considered this yet..
> > Cheers,
> >
> > Glen
> > 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
> >
> >
> >
> > --
> > Whatever you can do or imagine, begin it;
> > boldness has beauty, magic, and power in it.
> >
> > -- Goethe
> >
>



-- 
Whatever you can do or imagine, begin it;
boldness has beauty, magic, and power in it.

-- Goethe
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