Course Announcements (Tuesday 9/25)

Room: We have a bigger room! Reminder that going forward we are in 313 Fayerweather
Guest lecturer: Tomorrow’s guest lecturer is Brian Dalessandro, VP of Data Science at Media 6 Degrees (m6d). He will be teaching “Classification, Logistic Regression and Evaluation”. From their site, “Brian joined m6d as head of the data science team in September 2008. He has led the development of m6d’s patent pending machine learning technology, as the company has gone from pre-revenue to supporting 100s of concurrent client campaigns. His current research interests include building autonomous machine learning systems over big data architectures, causal inference and influence attribution. He recently served as co-chair of the 2012 KDD Cup competition. Prior to joining m6d, he was a Senior Research Analyst at Meetup.com, and a credit risk modeler for American Express. He holds an MBA with a concentration in Statistics from NYU and a BS in Mathematics and French Literature from Rutgers.”
Readings: Brian suggests reading Chapter 4 in Hastie & Tibshirani’s Elements of Statistical Learning, for a good intro to linear classifiers, and sent along two readings posted in Courseworks: An Empirical Comparison of Supervised Learning Algorithms and Data Mining in Metric Space: An Empirical Analysis of Supervised Learning Criteria (both by Caruana & Niculescu-Mizil). Brian writes: “One is an empirical evaluation of supervised learning algorithms. The other is a comparison of evaluation metrics. Both are by the same authors and the two papers highlight a theme that I’ll be pushing in the class – there is no one sized fits all solution for most problems and thus data science is a very empirical practice. Solving a problem requires understanding which evaluation metric to use and which algorithm will perform the best for that evaluation metric given the constraints you face. These two papers highlight differences of evaluation metrics and classifiers and also serve as an example of how to do meta-analysis in your search for the right solution.”
Computational Skills Workshop: Reminder to students that the workshop is this Friday & Saturday 9am-4:30pm More info given by email and in class. Please install Python before the workshop begins. Instructions are posted to Courseworks.
Happy Hour: Saturday, 5pm. Remember to RSVP in Courseworks.
Yom Kippur: For those students observing Yom Kippur who will miss class or be late, slides will be posted after the class.

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