uts.edu.auUTS CRICOS PROVIDER CODE: 00099F

VARIOUS FLAVOURS OF BIG DATA IN COMPUTER VISION, BANKING AND TEACHING

Massimo Piccardi

BIG DATA: SAMPLE SIZE

Data can be big because they are manye.g., AT&T’s trillion phone records

BIG DATA: DIMENSIONALITY

But data can also be big because each sample contains many values (dimensions)

e.g., a spam classification dataset with 16 trillion features [Weinberger 2009]

Large dimensionality large model, overfitting risk

x3x2 xDxi xjx1 ………

E[xixj]

BIG DATA: STRUCTURE

Or data can be big because their values are structuredStructure = factor graph (or others)

COMPUTER VISION: RECOGNISING ACTIONS

An interesting problem: recognising actions from still frames

superpixels: small, homogeneous regions in the image

recognition as relationships!

OUR APPROACH:

latent objects: “sky”, “road”, “desktop”, “coffee mug”…

OUR APPROACH

• Such a complex graph can be solved as a linear model!

• The graph is “flattened” into a one-dimensional array, , and scored as wT

• With a relaxed SVM solver, we have obtained an average precision of 72% over benchmark Stanford-40, a jump of 17 percentage points over the state of the art

COMPUTING THE SOLUTION

• Despite using a powerful computer cluster, training this model over 5,000 images takes over a month

• Where from here?

parallel solvers

AMPLAB, MLBASE, APACHE SPARK…

• MLbase: approximate, efficient SVM solution [Kraska 2013]

• 100x faster than Hadoop

TEACHING MACHINE LEARNING

• Started in 2004 with informal lecture series for doctoral students

• Flipped class last year (with player MS Silverlight, interactive & mobile-friendly)

• Teaching ML to industry audiences:

not sated by the bird’s eye view, very keen on the technical detail and how it actually all works

DATA SCIENCE: COMMONWEALTH BANK

FOLLOW UP?

Prof. Massimo PiccardiMassimo.Piccardi@uts.edu.au

Global Big Data Technology CentreUniversity of Technology, Sydney

http://www.bdt.uts.edu.au/