[ml] Stanford Machine Learning Course CS229
paul at pauloppenheim.com
Wed Aug 25 01:32:43 PDT 2010
I'm interested, but about to move to europe for several months, taking
me out of any face-to-face meetings. Could I recommend using the wiki
for class discussion and updates? That way others could follow along and
learn from what you do at their own pace, and beginners would have a
clear place to start.
On 2010-08-19 4:39 PM, Glen Jarvis 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..
> On Thu, Aug 19, 2010 at 3:36 PM, Joe Hale <joe at jjhale.com
> <mailto: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 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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> -- Goethe
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