STAT 598Z
Spring 2012
STAT 598Z Midterm
March 08, 2012
Time: 75 minutes
Your Name (Please print):
PUID (Please print):
Note:
1.
2.
3.
4.
5.
6.
7.
This exam will contribute 10 points towards your nal s
lecture 9: supervised learning
(contd)
STAT 598z: Introduction to computing for statistics
.
Vinayak Rao
Department of Statistics, Purdue University
February 10, 2015
.
Linear regression
We saw how to
lecture 13: tidy data
STAT 598z: Introduction to computing for statistics
.
Vinayak Rao
Department of Statistics, Purdue University
February 24, 2015
.
Homework 3
In the previous homework, we manually
lecture 15: hw and exam review
STAT 598z: Introduction to computing for statistics
.
Vinayak Rao
Department of Statistics, Purdue University
March 3, 2015
.
Plot USPS digits
plot_digit <- function(ip_
lecture 8: supervised learning
STAT 598z: Introduction to computing for statistics
.
Vinayak Rao
Department of Statistics, Purdue University
February 5, 2015
.
Supervised learning
We are given trainin
lecture 16: regular expressions in r
STAT 598z: Introduction to computing for statistics
.
Vinayak Rao
Department of Statistics, Purdue University
March 12, 2015
.
print and cat
We have seen the print
lecture 12: hw review & intro to plyr
STAT 598z: Introduction to computing for statistics
.
Vinayak Rao
Department of Statistics, Purdue University
February 19, 2015
.
Some observations from the homew
lecture 14: k-nn, k-means and k-fold
cross validation
STAT 598z: Introduction to computing for statistics
.
Vinayak Rao
Department of Statistics, Purdue University
February 26, 2015
.
k-nearest neighb
lecture 17: object-oriented
programming in r
STAT 598z: Introduction to computing for statistics
.
Vinayak Rao
Department of Statistics, Purdue University
March 23, 2015
.
Object oriented programming
lecture 18: regularization
STAT 598z: Introduction to computing for statistics
.
Vinayak Rao
Department of Statistics, Purdue University
March 26, 2015
.
Ordinary least squares
Consider linear regress
lecture 19: lasso and coordinate
descent
STAT 598z: Introduction to computing for statistics
.
Vinayak Rao
Department of Statistics, Purdue University
March 31, 2015
.
Bias-variance and regularization
lecture 6: the grammar of graphics
STAT 598z: Introduction to computing for statistics
.
Vinayak Rao
Department of Statistics, Purdue University
February 3, 2015
.
Grammar?
A set of rules describing h
lecture 23: monte carlo methods
(contd)
STAT 598z: Introduction to computing for statistics
.
Vinayak Rao
Department of Statistics, Purdue University
April 14, 2015
.
Importance sampling
Importance sa
lecture 25: review
STAT 598z: Introduction to computing for statistics
.
Vinayak Rao
Department of Statistics, Purdue University
April 21, 2015
.
Syllabus for Midterm 2
Regular expressions
Melting a
lecture 24: monte carlo methods
(contd)
STAT 598z: Introduction to computing for statistics
.
Vinayak Rao
Department of Statistics, Purdue University
April 16, 2015
.
Markov chain Monte Carlo
For reje
lecture 20: overview of optimization
STAT 598z: Introduction to computing for statistics
.
Vinayak Rao
Department of Statistics, Purdue University
April 2, 2015
.
Global and local minimum
Find minimum
lecture 21: monte carlo methods
STAT 598z: Introduction to computing for statistics
.
Vinayak Rao
Department of Statistics, Purdue University
April 7, 2015
.
Monte Carlo integration
We want to calcula
lecture 10: numerical issues in r
STAT 598z: Introduction to computing for statistics
.
Vinayak Rao
Department of Statistics, Purdue University
February 12, 2015
.
Binary representation
Most computati
lecture 11: debugging (i)
STAT 598z: Introduction to computing for statistics
.
Vinayak Rao
Department of Statistics, Purdue University
February 17, 2015
.
Bugs
Bugs are an inevitable part of programm
STAT 598Z
Spring 2012
STAT 598Z Midterm
March 08, 2012
Time: 75 minutes
Your Name (Please print):
PUID (Please print):
Note:
1.
2.
3.
4.
5.
6.
7.
This exam will contribute 10 points towards your nal s
Machine Learning Basics
Xiao Wang, Purdue University
Outline
Introduction
Probability and Information Theory
Numerical Computation
Machine Learning Basics
Statistics vs. Machine Learning
Statisti
Stats 598z: Midterm exam 2
Important:
Write you name and PUID on all sheets, and include the number of sheets
There are 7 questions, each for 5 points (but not all equally easy)
Attempt all questions,
Stats 598z: Midterm exam 1
Important:
Write you name and PUID on all sheets, and include the number of sheets
There are 8 questions, each for 5 points (but not all equally easy)
Attempt all questions,
Stats 598z: Homework 1
Problem 1 due before class on Jan 29
Important:R code, tables and gures should be part of a single .pdf or .html les from R
Markdown and knitr. See the class reading lists for a
Stats 598z: Homework 2
HW2 due before midnight on Feb 6
Important:
R code, tables and gures should be part of a single .pdf or .html les from R Markdown and
knitr. See the class reading lists for a sh
Stats 598z: Homework 3
Due before class on Tuesday, Feb 17
Important:
R code, tables and gures should be part of a single .pdf or .html les from R Markdown and
knitr. See the class reading lists for a
Stats 598z: Homework 4
Due before class on Tuesday, Mar 3
Important:
R code, tables and gures should be part of a single .pdf or .html les from R Markdown and
knitr. See the class reading lists for a
Stats 598z: Homework 6
Due before class Thursday, Apr 16
Important:
R code, tables and gures should be part of a single .pdf or .html les from R Markdown and
knitr. See the class reading lists for a s
Stats 598z: Homework 7
Due before class Thursday, Apr 30
Important:
R code, tables and gures should be part of a single .pdf or .html les from R Markdown and
knitr. See the class reading lists for a s