EE 4389W
Homework 5 (10 pts)
Due date: Dec 6, 2011
F 2011
Topic: SVM classification
This homework illustrates the use of SVM classification, and SVM model selection.
You can use any SVM s/w package (i
Introduction to
Predictive Learning
LECTURE SET 7
Support Vector Machines
Electrical and Computer Engineering
1
OUTLINE
Objectives
explain motivation for SVM
describe basic SVM for classification & re
Introduction to
Predictive Learning
LECTURE SET 2
Basic Learning Approaches and
Complexity Control
Electrical and Computer Engineering
1
OUTLINE
2.0 Objectives
2.1 Data Encoding + Preprocessing
2.2 Te
Introduction to
Predictive Learning
LECTURE SET 4
Statistical Learning Theory
Electrical and Computer Engineering
1
OUTLINE of Set 4
Objectives and Overview
Inductive Learning Problem Setting
Keep-It-
Introduction to
Predictive Learning
LECTURE SET 1
INTRODUCTION and OVERVIEW
Electrical and Computer Engineering
1
OUTLINE of Set 1
1.1 Overview: what is this course about:
- subject matter
- philosoph
Introduction to
Predictive Learning
LECTURE SET 3
Philosophical Perspectives
Electrical and Computer Engineering
1
OUTLINE
3.1 Overview of Philosophy
3.2 Epistemology
3.3 Acquisition of Knowledge and
EE 4389W Reading 5 Notes
All learning methods presented so far in this book follow a standard inductive learning setting,
where the goal is to estimate a predictive model from finite training data . T
EE 4389W Reading 4 Notes
This chapter describes a family of learning algorithms known as Support Vector Machines
(SVM). SVM methodology was developed in Statistical Learning Theory, and later was adop
EE 4389W Reading 3 Notes
This chapter describes data-analytic methods developed in the field of artificial neural networks
(ANNs). Neural network methods have been inspired by biological learning, and
EE 4389W Reading 2 Notes
The remaining chapters of this book describe various learning algorithms for estimating
predictive models from data. This chapter describes statistical methods for classificat
EE 4389W Reading 1 Notes
This chapter explores close relationship between predictive learning and relevant philosophical
ideas. This connection is not obvious, because predictive learning is described
1
EE 4389W
FALL 2011
Homework 1 (5 pts total)
Due Sep 15, 2011
BACKGROUND ON PROBABILITY AND PROGRAMMING
Problem 1 (1 point)
Consider a fair coin toss, so that the probability of the Head or Tails out
EE 4389W
Homework 2 (10 pts)
Due date: Oct 4, 2011
Fall 2011
Topic: Model selection and complexity control
This homework illustrates the use of resampling methods for model selection (complexity
contr
1
EE 4389
Homework 3 (10 pts)
Due date: Nov. 3, 2011
F 2011
Trading international mutual funds
Background: this HW applies predictive data-analytic methods to frequent trading of international
mutual
EE 4389W
Homework 4 (10 pts)
Due date: Nov 15, 2011
Topic: Nonlinear classification and regression methods
Fall 2011
Problem 1: Classification
(a) Estimate predictive model for presidential elections