[Comp-neuro] Machine Learning Summer School 2015, Sydney, Australia

Edwin Bonilla edwinb at cse.unsw.edu.au
Fri Dec 5 04:45:05 CET 2014

Call for Participation:

MLSS Sydney 2015: Machine Learning Summer School 2015, Sydney, Australia
February 16th to February 25th, 2015

Important dates (AEDT):
Scholarship application deadline: December 15th, 2014
Early bird registration deadline: January 15th, 2015
Late registration: January 16th, 2015 to January 31st, 2015


Machine Learning is a foundational discipline that forms the basis of much modern statistical data analysis. As data science emerges to meet the challenges of "Big Data", understanding the theory and practice of machine learning becomes a crucial asset in academia and industry.

The machine learning summer school provides graduate students, academics and industry professionals with an intense learning experience on the theory and applications of modern machine learning. Over the course of eight days, a panel of internationally renowned experts in the field will offer tutorials covering basic as well as advanced topics. In addition, MLSS Sydney 2015 will feature hands-on sessions aimed at reinforcing the learned concepts through practice.

The school will have a strong focus on probabilistic inference, large scale learning, Bayesian non-parametrics and applications to recommender systems, vision and document analysis.

Confirmed Speakers and Topics
Ryan Adams (Harvard University)
Bayesian Nonparametrics: Dirichlet Processes and Friends

Wray Buntine (Monash University)
Bayesian Non-parametric Methods for Unsupervised Models

Bob Carpenter (Columbia University)
Bayesian inference, MCMC and Stan Hands-on

Justin Domke (NICTA)
Probabilistic Graphical Models

Stephen Gould (Australian National University)
Structured Prediction for Computer Vision

Alex Ihler (UCI Irvine)
Approximate Inference

Mark Johnson (Macquarie University)
Natural Language Processing

Alexandros Karatzoglou (Telefonica)
ML for Recommender Systems

Neil Lawrence (The University of Sheffield)
Bayesian Nonparametrics: Gaussian Processes

Frank Nielsen (Ecole Polytechnique)
Computational Information Geometry and Machine Learning

Richard Nock (NICTA)

Lizhen Qu (NICTA )
Deep Learning

Mark Reid (ANU and NICTA)
Prediction Markets

Mark Schmidt (University of British Columbia)

Chris Webers (NICTA)
Introduction to Machine Learning

Edwin V. Bonilla, The University of New South Wales
Fabio Ramos, The University of Sydney
Yang Wang, NICTA

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