Thought this was cool: SML: Scalable Machine Learning
Comments: “SML: Scalable Machine Learning
SML: Scalable Machine Learning
COMPUTER SCIENCE C281B
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
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
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.