Lecture 4
The nearest neighbor classifiers
The nearest neighbor rule
A set of n pairs (x1, t1),., (xn, tn) is given,
where xi takes real values and ti takes values
in the set cfw_1, ., M . Each xi is the outcome
of the set of measurements made upon the it

Maximum Likelihood Estimation
C. Vasantha Lakshmi
Probability
the probabilities of different outcomes for a
certain event must always add up to 1:
if there is a 20% chance of rain today, there
must be an 80% chance of no rain.
Another Law
if two events

Pattern
Classification
All materials in these slides were taken from
Pattern Classification (2nd ed) by R. O.
Duda, P. E. Hart and D. G. Stork, John Wiley
& Sons, 2000
with the permission of the authors and the
publisher
Chapter 2 (Part 1):
Bayesian Decis

BAYESIAN PARAMETER ESTIMATION
95% confidence interval:
A range of numbers obtained with a
method that, if repeated over and over
again, would contain the true value of a
parameter 95% of the time.
p-value:
The probability of unobserved data that
are mor

Pattern
Classification
All materials in these slides were taken from
Pattern Classification (2nd ed) by R. O. Duda, P.
E. Hart and D. G. Stork, John Wiley & Sons, 2000
with the permission of the authors and the
publisher
Chapter 1: Introduction to Pattern

Lecture 6 : The Normal Distribution
Jonathan Marchini
Continuous data
In previous lectures we have considered discrete
datasets and discrete probability distributions. In practice many datasets that we collect from experiments
consist of continuous measur

1
Pattern
Classification
All materials in these slides were taken
from
Pattern Classification (2nd ed) by R. O.
Duda, P. E. Hart and D. G. Stork, John Wiley
& Sons, 2000
with the permission of the authors and the
publisher
Pattern Classification, Chapter