[ml] Stanford Machine Learning Course CS229
thomas.lotze at gmail.com
Thu Aug 19 16:24:00 PDT 2010
This sounds like a great idea. I'm definitely interested.
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
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