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Statistical Learning and Data Science

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There is a limited waitlist for this short course. You will be contacted if you are on the waitlist and a place becomes available.

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Red Centre, Centre Wing, Room 4082

UNSW Kensington Campus, NSW 2052

Australia

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Sales Have Ended

Registrations are closed
There is a limited waitlist for this short course. You will be contacted if you are on the waitlist and a place becomes available.
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Statistical Learning and Data Science

Note: This is a (free!) short course over five weeks with one 90-minute session per week. There are no repeat sessions. If you would like to participate, you should try to come to all five sessions. Sessions are on consecutive Wednesdays from 2:00 pm to 3:30 pm. The course starts on Wednesday 21st February and ends on Wednesday 21st March.

We're expecting strong interest in this course so please book early, but also please tell us if you need to cancel your booking so we can offer the place to people who may be waitlisted.


Presenter: Trevor Hastie
John A. Overdeck Professor of Statistics, Stanford University, and SHARP Professor, School of Mathematics and Statistics, UNSW Sydney

Abstract:
We give an overview of statistical models used by data scientists for prediction and inference. With the rapid developments in internet technology, genomics, financial risk modelling, and other high-tech industries, we rely increasingly more on data analysis and statistical models to exploit the vast amounts of data at our fingertips. We then focus on several important classes of tools. For wide data, we have a closer look at the lasso and its relatives, and for tall data random forests and boosting. We also review support-vector machines and the recent advances in deep learning. Most of the material can be found in
An Introduction to Statistical Learning, with Applications in R
by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani (Springer, 2013)
and
Elements of Statistical Learning
by Trevor Hastie, Robert Tibshirani and Jerome Friedman (Springer 2009).


Both are available for free as pdf files from Trevor’s website
http://web.stanford.edu/~hastie/pub.html


The format of the course and a more detailed list of topics will be available closer to the starting date. We suggest you bring your own laptop (fully charged!).

Afternoon tea will be provided after each session where informal discussion can continue.




About the presenter:
Trevor Hastie authored two key texts found on the shelves of most applied statisticians: Generalized Additive Models (with Rob Tibshirani), and Elements of Statistical Learning (with Rob Tibshirani and Jerry Friedman). He has made significant contributions to statistical computing, developing software libraries (in S and more recently in R) which form the foundations of many statistical modelling tools in use today. His current research focuses on applied problems in biology and genomics, medicine and industry, in particular data mining, prediction and classification problems. He has been a Professor in Statistics and Biostatistics at Stanford University since 1994, prior to a stint at AT&T Bell Laboratories, and now also holds a part-time SHARP Professorial appointment at UNSW Sydney in the School of Mathematics and Statistics.

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Red Centre, Centre Wing, Room 4082

UNSW Kensington Campus, NSW 2052

Australia

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