Summary Overview about some methods of machine learing

Machine Learning 2007: Lecture 11

Instructor: Tim van Erven ()
Website: www.cwi.nl/˜erven/teaching/0708/ml/
November 28, 2007






, Overview

Organisational
Matters
● Organisational Matters
Models ● Models
Maximum Likelihood ● Maximum Likelihood Parameter Estimation
Parameter Estimation
● Probability Theory
Probability Theory
● Bayesian Learning
Bayesian Learning
✦ The Bayesian Distribution
✦ From Prior to Posterior
✦ MAP Parameter Estimation
✦ Bayesian Predictions
✦ Discussion
✦ Advanced Issues






,Organisational
Matters Guest lecture:
Models ● Next week, Peter Grünwald will give a special guest lecture
Maximum Likelihood
Parameter Estimation about minimum description length (MDL) learning.
Probability Theory
This Lecture versus Mitchell:
Bayesian Learning
● Chapter 6 up to section 6.5.0 about Bayesian learning.
● I present things in a better order.
● Mitchell also covers the connection between MAP parameter
estimation and least squares linear regression: It is good for
you to study this, but I will not ask an exam question about it.






, Overview

Organisational
Matters
● Organisational Matters
Models
● Models
Maximum Likelihood ● Maximum Likelihood Parameter Estimation
Parameter Estimation
● Probability Theory
Probability Theory
● Bayesian Learning
Bayesian Learning
✦ The Bayesian Distribution
✦ From Prior to Posterior
✦ MAP Parameter Estimation
✦ Bayesian Predictions
✦ Discussion
✦ Advanced Issues