[其他]Statistical Learning By Trevor Hastie & Rob Tibshirani #9 - 2014-01-21@Stanford
本帖最后由 EroicaCMCS 于 2014-2-27 00:54 编辑 |
以前有老师推荐过这本教材 An Introduction to Statistical Learning, with Applications in R，可惜自学没坚持下来，现在有了作者大S教授亲自开公开课，那大家还等什么，还不来速速报名，尤其对Machine Learning有兴趣的童鞋！http://online.stanford.edu/course/statistical-learning-winter-2014
This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical).
This is not a math-heavy class, so we try and describe the methods without heavy reliance on formulas and complex mathematics. We focus on what we consider to be the important elements of modern data analysis. Computing is done in R. There are lectures devoted to R, giving tutorials from the ground up, and progressing with more detailed sessions that implement the techniques in each chapter.
The lectures cover all the material in An Introduction to Statistical Learning, with Applications in R by James, Witten, Hastie and Tibshirani (Springer, 2013). As of January 5, 2014, the pdf for this book will be available for free, with the consent of the publisher, on the book website.
Instructor(s)Trevor Hastie Trevor Hastie is the John A Overdeck Professor of Statistics at stanford University. Hastie is known for his research in applied statistics, particularly in the fields of data mining, bioinformatics and machine learning. He has published four books and over 180 research articles in these areas. Prior to joining Stanford University in 1994, Hastie worked at AT&T Bell Laboratories for 9 years, where he helped develop the statistical modeling environment popular in the R computing system. He received his B.S. in statistics from Rhodes University in 1976, his M.S.
Rob Tibshirani Robert Tibshirani is a Professor in the Departments Health Research and Policy and Statistics at Stanford University. In his work he has made important contributions to the analysis of complex datasets, most recently in genomics and proteomics. His most well-known contribution is the Lasso, which uses L1 penalization in regression and related problems. He has co-authored over 200 papers and three books. Professor Tibshirani co-authored the first study that linked cell phone usage with car accidents, a widely cited article that has played a role in the introduction of legislation that restricts the use of phones while driving. He is one of the most widely cited authors in the entire mathematical sciences field. Professor Tibshirani is a Fellow of the American Statistical Association, the Institute of Mathematical Statistics and the Royal Society of Canada. He won the prestigious COPSS Presidents's award in 1996, the NSERC Steacie award in 1997 and was elected to the National Academy of Sciences in 2012.
Week 1: Introduction and Overview of Statistical Learning (Chapters 1-2, starts Jan 21)http://www.1point3acres.com/bbs/thread-81118-1-1.html 讨论作业贴
Week 2: Linear Regression (Chapter 3, starts Jan 25)
Week 3: Classification (Chapter 4, starts Feb 1)
Week 4: Resampling Methods (Chapter 5, starts Feb 8)
Week 5: Linear Model Selection and Regularization (Chapter 6, starts Feb 15)
Week 6: Moving Beyong Linearity (Chapter 7, starts Feb 22)
Week 7: Tree-based Methods (Chapter 8, starts Mar 1)
Week 8: Support Vector Machines (Chapter 9, starts Mar 8)
Week 9: Unsupervised Learning (Chapter 10, starts Mar 15)