STATISTICAL LEARNING AND DATA SCIENCE
In this course we emphasize the tools useful for tackling modern-day data analysis problems. Many of these are essential building blocks, but we also include techniques at the cutting-edge of technology for handling big-data problems. From the vast array of tools available, we have selected what we consider are the most relevant and exciting. Our list of topics include:
Our earlier courses are not a prerequisite for this new course. Although there is overlap with past courses, our new course contains topics not covered by us before. We illustrate many of the methods using examples developed in R.
The material is based on recent papers by the authors and other researchers, as well as our best selling books:
Professors Hastie and Tibshriani are both members of the Statistics and Biomedical Data Science Departments at Stanford University. They have collaborated on research projects over their entire careers, and have coauthored several books: Generalized Additive Models (1990), Elements of Statistical Learning (2001, second edition 2009, also with J. Friedman), Introduction to Statistical Learning (2013, also with G. James and D. Witten), and Statistical Learning with Sparsity (2015, also with M. Wainwright).
Professor Hastie spent his first eight years post-PhD with the Statistics and Data Analysis Research group, AT&T Bell Laboratories, where he gained valuable experience with prediction problems in industry and manufacturing. He has published extensively in the area of nonparametric regression and classification. He co-edited the Wadsworth book Statistical Models in S (1991) with John Chambers. His Ph.D. thesis Principal Curves introduced one of the first nonlinear versions of principal components analysis. During his ten years at Bell Laboratories
Professor Tibshirani is a recipient of the COPSS award - an award given jointly by all the leading statistical societies to the most outstanding statistician under the age of 40. He also has many research articles on nonparametric regression and classification. With Bradley Efron he co-authored the best-selling text An Introduction to the Bootstrap in 1993, and has been an active researcher on bootstrap technology over the years. His 1984 Ph.D thesis spawned the currently lively research area known as Local Likelihood. He has more than thirty five years experience in consulting on biostatistical problems.
Professors
Hastie and Tibshirani published "The Elements of Statistical learning:
Data mining, inference and prediction", with Jerome Friedman
(springer, 2001, second edition 2009). This book has received a terrific reception,
with over 45,000 copies sold. Both presenters are actively involved in research in
statistical learning methods, and are well-known not
only in the statistics community but in the machine-learning,
neural network and bioinformatics fields as well.
Their newer book "An Introduction to Statistical Learning, with Applications in R" (with Gareth James and Daniela Witten, 2013) is also a best-seller, and has remained consistently in the top 10 in the Amazon categories "Mathematics and Statistics" and "Artificial Intelligence", with a five-star rating based on 84 customer reviews.
Over the
years they have become leaders in the statistical analysis of DNA
microarrays, working with leading-edge biologists such as Patrick
Brown of Stanford University, and David Botstein of
Princeton. They have given many short courses together over the
past 20 years, to academic, government and industrial
audiences. They are both actively involved with consulting in data
analysis and modeling, for the Stanford medical community as well
as local biotech and web-related industries. They have a
reputation for being good instructors who interact well with the
needs of the audience.
Read here for more details on
who should
attend, and our
policy
not to sell our course notes.
http://hastie.su.domains/slds.html