CS195f Homework 3
Mark Johnson and Erik Sudderth Homework due at 2pm, 5th November 2009
This problem set asks you to investigate exponential or Maximum Entropy classiers. These involve probability distributions of the form: P(y | x) = Zx (w) =
y Y
1 exp (
CS195f Homework 4
Mark Johnson and Erik Sudderth Homework due at 2pm, 20th October 2009
In this problem set, we study dierent approaches to linear regression using a onedimensional dataset collected from a simulated motorcycle accident. The input variable
CS195f Homework 2
Mark Johnson and Erik Sudderth Homework due at 2pm, 1st October 2009
The rst question asks you to analyse the following naive Bayes model that describes the weather in a mythical country. Y X1 X2 P(X1 , X2 , Y ) P(Y =day) P(X1 =hot | Y =
CS195f Homework 1: Naive Bayes
Mark Johnson and Erik Sudderth Homework due at 2pm, 24th September 2009
The Nursery database records a series of admission decisions to a nursery in Ljubljana, Slovenia. We downloaded this data from http:/archive.ics.uci.edu
PATTERN RECOGNITION AND MACHINE LEARNING
CHAPTER 3: LINEAR MODELS FOR REGRESSION
Linear Basis Function Models (1)
Example: Polynomial Curve Fitting
Linear Basis Function Models (2)
Generally
where j(x) are known as basis functions. Typically, 0(x) = 1, so
PATTERN RECOGNITION AND MACHINE LEARNING
CHAPTER 2: PROBABILITY DISTRIBUTIONS
Parametric Distributions
Basic building blocks: Need to determine given Representation: or ?
Recall Curve Fitting
Binary Variables (1)
Coin flipping: heads=1, tails=0
Bernoulli
PATTERN RECOGNITION AND MACHINE LEARNING
CHAPTER 1: INTRODUCTION
Example
Handwritten Digit Recognition
Polynomial Curve Fitting
Sum-of-Squares Error Function
0th Order Polynomial
1st Order Polynomial
3rd Order Polynomial
9th Order Polynomial
Over-fitting
Pattern Recognition and Machine Learning Errata and Additional Comments
Markus Svens n and Christopher M. Bishop e September 9, 2009
2
Preface
This document lists corrections and clarications for the rst printing1 of Pattern Recognition and Machine Learni
CSCI 1950-F: Introduction to Machine Learning
Erik Sudderth and Mark Johnson, Fall 2009
How can artificial systems learn from examples, and discover information buried in massive datasets? This course explores the theory and practice of statistical machin