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"Small Data, Rich Data" + "Using Machine Learning to Predict the US Election"

2 speakers: 1st talk is on Exploratory Analysis and Visualization. 2nd talk is on using Machine Learning & Statistics to make Predictions.

The first talk is by Elena Popovici:

"Small Data, Rich Data: Exploratory Analysis and Visualization"

"Earlier this year, the US Medicare health organization (similar to the Canadian MSP, but mostly just for seniors) has released information about how much hospitals across the country charge the program. The dataset is relatively small and appears simple at first glance. Closer analysis reveals a rich structure and many peculiarities. Visualization plays a key role in exploring the nature of the data. The talk will cover approach, tools used (heavy on R and extensions) and findings."

The second talk is by Kazem Jahanbakhsh:

"How I Used Machine Learning & Statistics To Predict The US Presidential Election"

"US 2012 presidency election generated a large number of conversations in social networking websites such as Twitter. In September 2012, we started collecting & analyzing political tweets to see if we can find any interesting pattern/trend in the data. In particular, we were interested in the possibility of predicting US election result by analyzing tweets distributions. Our methodology was simple: (1) collect data, (2) use machine learning & statistics to analyze data, (3) visualize the results to get insight. This talk covers our methodology and some of our findings."


Schedule
• 6:00PM Doors are open, feel free to mingle
• 6:30 Presentations start
• 8:00 Off to a nearby watering hole (Mr. Brownstone?) for a pint, food, and/or breakout discussions

Getting There

By transit there a number of high frequency buses (check Google Maps or the Translink site for your particular case) that will get you there. For the drivers, there is a fair bit of street parking (free and pay) in the area, especially after 6.


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