Statistics 315a
Homework 1, due Wednesday January 28, 2015.
ESL refers to the course textbook, and ESL 2.4 refers to exercise 2.4 in
ESL. Since the homework assignments count 70% of your nal grade, you
must do them on your own. Problem 1 is computing inte
Stats 315B Homework 1 Solutions
Problem 1
First build a large tree so we can use rparts cross-validation feature to determine the optimal tree size. The cross-validation errors can be found using
the function printcp and used to nd the optimal value of th
Stats315B Homework 2
Spring 2015
Due: May 21, 2015
Please keep answers short. Verbosity will not be rewarded.
1. Random forests predict with an ensemble of bagged trees each trained on a bootstrap
sample randomly drawn from the original training data. Add
Stat315B
Methods for Applied Statistics
Homework 3
Due date: Friday, June 5, 2015.
1. Consider a multi-hidden layer neural network trained by sequential steepest-descent using the
weight updating formula
wt = wt1 G(wt1 ).
Here t labels the observations pr
ESL Chapter 3 Linear Methods for Regression
Trevor Hastie and Rob Tibshirani
Linear Methods for Regression
Outline
The simple linear regression model
Multiple linear regression
Model selection and shrinkagethe state of the art
1
ESL Chapter 3 Linear Me
SLDM III c Hastie & Tibshirani - March 18, 2010
Dimension Reduction and SVD
Principal Components
Suppose we have N measurements on each of p variables
Xj , j = 1, . . . , p. There are several equivalent approaches to
principal components:
Produce a deriv
ESL Chapter 2 Overview of Supervised Learning
Trevor Hastie and Rob Tibshirani
Overview of Supervised Learning
Notation
X: inputs, feature vector, predictors, independent variables.
Generally X will be a vector of p real values. Qualitative features are
Stats315a: Statistical Learning
1
Statistics in the news
How IBM built Watson, its Jeopardy-playing
supercomputer by Dawn Kawamoto DailyFinance 02/08/2011
Learning from its mistakes
According to David Ferrucci
(PI of Watson DeepQA technology for IBM Resea