[ml] Stanford Machine Learning Course CS229
joe at jjhale.com
Fri Aug 20 16:43:45 PDT 2010
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,
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
> * 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..
> On Thu, Aug 19, 2010 at 3:36 PM, Joe Hale <joe at jjhale.com> wrote:
>> 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
>> 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
>> 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.
>> 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:
>> 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
>> 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
> Whatever you can do or imagine, begin it;
> boldness has beauty, magic, and power in it.
> -- Goethe
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