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VIDEO lecture: First steps in scientific data visualisation using d3.js - by Drew Conway

Mike Dewar (Data Scientist, bit.ly), presents a talk on getting started with data driven design in Javascript to the New York Open Statistical Programming Meetup on Jan. 12, 2012. Mike Bostock's d3 javascript library has lately taken the internet by storm, being the engine underlying a very beautiful set of visualisations (mbostock.github.com/d3/). Because of this, many have investigated d3.js as a potential addition to their current visualisation stack, only to fall over one of some common hurdles. This talk will demonstrate how to clear these first few hurdles, including:
- how to create and serve nice data objects
- how to use chrome's console to inspect and play with your visualisation,
- how d3 interacts with the document object model,
- how to draw arbitrary SVG objects,
- how to use d3.layout to relieve you of a few common graph-vis tasks.

The talk will be useful to those who are curious about using d3.js and wants to get started making interactive and dynamic statistical visualisations. You can download the slides (all written with d3.js) from Mike Dewar's Github: github.com/mikedewar/d3talk

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NIPS Workshop on Optimization for Machine Learning, Whistler 2008 - Video Lectures

Classical optimization techniques have found widespread use in machine learning. Convex optimization has occupied the center-stage and significant effort continues to be still devoted to it.

 

Pattern Analysis, Statistical Modelling and Computational Learning » NIPS Workshop on Optimization for Machine Learning, Whistler 2008.

 

Training a Binary Classifier with the Quantum Adiabatic Algorithm

 

Polyhedral Approximations in Convex Optimization

 

Optimization in Machine Learning: Recent Developments and Current Challenges

 

Large-scale Machine Learning and Stochastic Algorithms

 

Online and Batch Learning Using Forward-Looking Subgradients

 

Robustness and Regularization of Support Vector Machines

 

Tuning Optimizers for Time-Constrained Problems using Reinforcement Learning.

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