# SML -- Scalable Machine Learning Course

SML: Scalable Machine LearningPractical informationSTATISTICS 241B, COMPUTER SCIENCE C281BUpdates

Volume:3 hours per week (3 credits)

Time:Tuesday, 4-7pm (3 lectures /in one block)

Location:306 SODA

Instructor:Alex Smola (available 1-3pm Tuesdays in Evans 418)

TA: Dapo Omidiran

Grading Policy: Assignments (40%), Project (50%), Midterm project review (10%), Scribe (Bonus 5%)

Piazzadiscussion boardOverview

02222012 - Slides are online

02222012 - New assignments are live

02222012 - Video for SVM (first three sets) are uploaded

02222012 - Video for Optimization complete

02052012 - Slides for Streams and Optimization are uploaded

02052012 - Videos now have sound enabled

01252012 - Problem set 1 is uploaded

01252012 - Slides and videos are uploaded

01252012 - Project ideas and datasets are uploaded

01192012 - The graphical models tab has links to video lectures on tutorials on the subject (this is mainly for students who didn’t get to attend the class by Mike Jordan and Martin Wainwright).

01182012 - The systems slides are available now (follow the systems link)

01182012 - Updated project guidelines

Scalable Machine Learning occurs when Statistics, Systems, Machine Learning and Data Mining are combined into flexible, often nonparametric, and scalable techniques for analyzing large amounts of data at internet scale. This class aims to teach methods which are going to power the next generation of internet applications.

The class will cover systems and processing paradigms, an introduction to statistical analysis, algorithms for data streams, generalized linear methods (logistic models, support vector machines, etc.), large scale convex optimization, kernels, graphical models and inference algorithms such as sampling and variational approximations, and explore/exploit mechanisms. Applications include social recommender systems, real time analytics, spam filtering, topic models, and document analysis.

Resources Prerequisites

Basic probability and statistics. Having attended a machine class would be a big plus but is not absolutely required. Particularly some knowledge of kernels and graphical models would be useful.

Basic linear algebra (matrices, vectors, eigenvalues). Knowing functional analysis would be great but not required.

Ability to write code that exceeds ‘Hello Worldâ€™. Preferably beyond Matlab or R.

Basic knowledge of optimization. Having attended a convex optimization class would be great.

Page generated 2012-02-22 21:44:22 PST, by jemdoc.

Looks like some really awesome content in here.