Dr. Daniela Witten from the University of Washington presents a lecture titled "Supervised Machine Learning."

View Slides
https://drive.google.com/open?id=0B4I...

Lecture Abstract
In this lecture, Dr. Witten will talk about big-picture challenges and solutions for supervised learning from biomedical big data. She will cover the concepts of training set versus validation set error, and will review cross-validation and related approaches to estimate validation set error. Several specific supervised learning
approaches will be presented, including regularization approaches (such as the lasso and ridge regression), tree-based approaches, and support vector machines.

About the Speaker
Daniela Witten's research involves the development of statistical machine learning methods for high-dimensional data, with applications to genomics and other fields. She is particularly interested in unsupervised learning, with a focus on graphical modeling. Daniela is the recipient of a number of honors, including a NDSEG Research Fellowship, an NIH Director's Early Independence Award, a Sloan Research Fellowship, and an NSF CAREER Award. Her work has been featured in the popular media: among other forums, in Forbes Magazine (three times), Elle Magazine, on KUOW radio, and as a PopTech Science Fellow. Daniela is a co-author (with Gareth James, Trevor Hastie, and Rob Tibshirani) of the very popular textbook "Introduction to Statistical Learning". She was a member of the Institute of Medicine committee that released the report "Evolution of Translational Omics". Daniela completed a BS in Math and Biology with Honors and Distinction at Stanford University in 2005, and a PhD in Statistics at Stanford University in 2010. Since 2014, Daniela is an associate professor in Statistics and Biostatistics at University of Washington.

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