Edmonton Data Science Meetup

Edmonton Data Science meetup is a place to learn about data science and network with fellow Data Scientists. The presentations for the upcoming meetup include:

• "Cost-Sensitive Classifier Evaluation Using Cost Curves" - Professor Robert Holte (CS @ UofA)
• "Figuring out the limits of human vision with deep learning" - Navaneeth Kamballur Kottayil (Machine Learning Researcher at BorealisAI)

Pizza will be provided before presentations and after presentations we will head to a pub for networking!

The meetup is sponsored by AltaML (http://altaml.com/).

Abstracts:

Cost-Sensitive Classifier Evaluation Using Cost Curves
(co-author: Chris Drummond)
The evaluation of classifier performance in a cost-sensitive setting is straightforward if the operating conditions (misclassification costs and class distributions) are fixed and known. When this is not the case, evaluation requires a method of visualizing classifier performance across the full range of possible operating conditions. This talk outlines the most important requirements for cost-sensitive classifier evaluation, and introduces a technique for classifier performance visualization – the cost curve – that meets all these requirements. This talk should be of interest to anyone who works in areas that use classifiers, for example, machine learning, pattern recognition, biometrics, and diagnosis.
Related papers:
http://dx.doi.org/10.1007/s10994-006-8199-5
https://webdocs.cs.ualberta.ca/~holte/Publications/kdd2000.pdf

Figuring out the limits of human vision with deep learning
In this talk, I present a part of my PhD thesis done in collaboration with Telecom Paristech. In this work, we explored methods to derive the human perceptual limit to see changes in an image. We proposed a new method to solve the problem with convolutional neural network. Our solution involved using deep learning in conjunction with traditional methods in psychophysics. Our method derives the human error detection threshold by solving an alternative problem of image quality assessment. We tested our method on publicly available databases for measuring human visual thresholds and show state of the art results.
Related papers:
https://arxiv.org/abs/1712.07269
https://www.researchgate.net/publication/327994999_Learning_Local_Distortion_Visibility_from_Image_Quality

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Edmonton Data Science (EDS) meetups are a platform for data science enthusiasts and professionals to:

• learn about big data techniques and tools

• discuss best practices in big data and data science

• promote big data and data science among university students and the local tech community

• learn about practical applications of data science methods

• learn about companies that use data science as the foundation of their business processes

We are passionate about data science and big data. We hope to create a vibrant data science community that is engaged in sharing and learning different topics in the field.

In these meetups, we intend to a) share best practices and knowledge of data science-based startups and corporations with university students, b) introduce research works on big data and data science to the local tech community. We aim to connect the students to companies and make them more aware of our vibrant local tech community. We also invite professors and researchers to present their work on data mining and machine learning methods to the professional community. Each meetup will include a presentation introducing a company who is using data science techniques, and two presentations about big data, data science and machine learning techniques.