Introduction to Machine Learning and Pattern Recognition
EECS 5327

Fall 2013
Last updated: Nov 26, 2012
MULTILAYER PERCEPTRONS
J. Elder
CSE 4404/5327 Introduction to Machine Learning and Pattern Recognition
Outline
Multilayer Perceptrons
2
Combining Linear Classifiers
Learning Parameters
CSE 4404/5327 Introduction to Machine Lear
Introduction to Machine Learning and Pattern Recognition
EECS 5327

Fall 2013
Last updated: Sept 20, 2012
MULTIVARIATE NORMAL
DISTRIBUTION
J. Elder
CSE 4404/5327 Introduction to Machine Learning and Pattern Recognition
Linear Algebra
Probability & Bayesian Inference
2
Tutorial this Wed 3:00 4:30 in Bethune 228
Linear Algebra Revie
Introduction to Machine Learning and Pattern Recognition
EECS 5327

Fall 2013
DIMENSIONALITY REDUCTION
J. Elder
CSE 4404/5327 Introduction to Machine Learning and Pattern Recognition
Credits
Probability & Bayesian Inference
2
Some of these slides were sourced and/or modified
from Simon Prince, University College London
CSE 4404/532
Introduction to Machine Learning and Pattern Recognition
EECS 5327

Fall 2013
Last updated: Nov 26, 2012
BOOSTING
J. Elder
CSE 4404/5327 Introduction to Machine Learning and Pattern Recognition
Limitations of Classifiers
Boosting
2
All of the classifiers we have studied have limitations: if the
problem is interesting, they dont ten
Introduction to Machine Learning and Pattern Recognition
EECS 5327

Fall 2013
Last updated: September 17, 2012
BAYESIAN DECISION THEORY
J. Elder
CSE 4404/5327 Introduction to Machine Learning and Pattern Recognition
Problems
Probability & Bayesian Inference
2
The following problems from the textbook are
relevant:
2.1
2.9, 2.11, 2
Introduction to Machine Learning and Pattern Recognition
EECS 5327

Fall 2013
Last Updated: November 22, 2012
KERNEL METHODS
J. Elder
CSE 4404/5327 Introduction to Machine Learning and Pattern Recognition
Kernel Methods: Outline
Kernel Methods
2
Generalized Linear Models
Radial Basis Function Networks
Support Vector Machines
Sep
Introduction to Machine Learning and Pattern Recognition
EECS 5327

Fall 2013
LINEAR REGRESSION
J. Elder
CSE 4404/5327 Introduction to Machine Learning and Pattern Recognition
Credits
Probability & Bayesian Inference
2
Some of these slides were sourced and/or modified
from:
Christopher
Bishop, Microsoft UK
CSE 4404/5327 Introducti
Introduction to Machine Learning and Pattern Recognition
EECS 5327

Fall 2013
Last updated: November 6, 2012
MIXTURE MODELS AND EM
J. Elder
CSE 4404/5327 Introduction to Machine Learning and Pattern Recognition
Credits
Probability & Bayesian Inference
2
Some of these slides were sourced and/or modified
from:
Christopher
Bishop, Mi
Introduction to Machine Learning and Pattern Recognition
EECS 5327

Fall 2013
Last updated: Oct 22, 2012
LINEAR CLASSIFIERS
J. Elder
CSE 4404/5327 Introduction to Machine Learning and Pattern Recognition
Problems
Probability & Bayesian Inference
2
Please do Problem 8.3 in the textbook. We will
discuss this in class.
CSE 4404/5327 I
Introduction to Machine Learning and Pattern Recognition
EECS 5327

Fall 2013
COURSE INTRODUCTION
J. Elder
CSE 4404/5327 Introduction to Machine Learning and Pattern Recognition
What is Machine Learning?
Probability & Bayesian Inference
2
Machine learning is the study of algorithms that
learn how to perform a task from prior experi