[ml] Stanford Machine Learning Course CS229

Joe Hale joe at jjhale.com
Thu Aug 19 22:36:57 UTC 2010


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.
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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