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SML: Scalable Machine Learning


Practical information

  • 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%)

  • Piazza discussion board


  • 041812 New set of assignments is online.

  • 041812 Slides for graphical models are online.


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.



  • 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 World’. Preferably beyond
    Matlab or R.

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

from Hacker News 50:


Written by cwyalpha

八月 9, 2012 在 5:09 下午

发表在 Uncategorized


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