American College of Computer & Information Sciences
AI
NONE 201

Spring 2011
Problem Set 2
MAS 622J/1.126J: Pattern Recognition and Analysis
Due: 5:00 p.m. on September 30
[Note: All instructions to plot data or write a program should be carried
out using Matlab. In order to maintain a reasonable level of consistency and
simplicit
American College of Computer & Information Sciences
AI
NONE 201

Spring 2011
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American College of Computer & Information Sciences
AI
NONE 201

Spring 2011
All Parts Included In AI Exam
Lets Start >
Nbd2 Be el Mal8y
Not to Study :
1. Alpha. Beta Pruning
2. Backtracking Pseudo Code Only
3.Nueral Network
4.Travel Sales man
5.8Puzzle
6.Fuzzy Logic
Question: Describe The Expert System Architecture ?
Another
American College of Computer & Information Sciences
AI
NONE 201

Spring 2011
FIRST and FOLLOW Robb T. Koether Left Factoring TableDriven LL Parsing
Nullability The FIRST Function The FOLLOW Function
FIRST and FOLLOW
Lecture 8 Section 4.4 Robb T. Koether
HampdenSydney College
Assignment
Mon, Feb 9, 2009
Outline
FIRST and FOLLOW R
American College of Computer & Information Sciences
AI
NONE 201

Spring 2011
CS771 Local Area Networks
by Harry T. Hsu Johns Hopkins University Montgomery County Graduate Center
CS771
I.Introduction
LAN Definition IEEE 802 Committee Definition
" .
a data communication system allowing a number of independent devices to communicate
American College of Computer & Information Sciences
AI
NONE 201

Spring 2011
Data and Computer
Communications
Communications
Chapter 7 Data Link Control
Chapter
Protocols
Protocols
Data Link Control Protocols
Link
need layer of logic above Physical
to manage exchange of data over a link
to
of
frame synchronization
flow control
e
American College of Computer & Information Sciences
AI
NONE 201

Spring 2011
Edge Detection
Hao Huy Tran
Computer Graphics and Image Processing
CIS 581 Fall 2002 Professor: Dr. Longin Jan Latecki
Edge Detection
What are edges in an image? Edge Detection Edge Detection Methods Edge Operators Matlab Program Performance
What are edg
American College of Computer & Information Sciences
AI
NONE 201

Spring 2011
Lecture 9
Lecture
Image Enhancement:
Enhancement in the
Frequency Domain
Outline
Lowpass Filtering
Highpass Filtering
Homomorphic Filtering
Le c t ur e 9  I ma ge Enha nc e me nt :
Enha nc e me nt i n t he Fr e que nc y Doma i n
2
Frequency Domain Met
American College of Computer & Information Sciences
AI
NONE 201

Spring 2011
September 28th, 2005
EE E6887 (Statistical Pattern Recognition)
Solutions for homework 1
P.1a Suppose two equally probable 1dimensional densities are of the form
p(xi) exp (x ai /bi ) , for i = 1, 2, and bi > 0
(a) write an analytic expression for eac
American College of Computer & Information Sciences
AI
NONE 201

Spring 2011
Intro to Pattern Recognition :
Pattern
Bayesian Decision Theory
2. 1 Introduction
2.2 Bayesian Decision TheoryContinuous
Features
Materials used in this course were taken from the textbook Pattern Classification by Duda et al., John Wiley & Sons, 2001
wit
American College of Computer & Information Sciences
AI
NONE 201

Spring 2011
Bayesian Decision Theory
Bayesian Decision Theory is a fundamental
statistical approach that quanties the trade offs
between various decisions using probabilities and
costs that accompany such decisions.
First, we will assume that all probabilities are
kn
American College of Computer & Information Sciences
AI
NONE 201

Spring 2011
INTRODUCTION TO SIMULATION
WHAT IS SIMULATION?
The imitation of the operation of a realworld process or
system over time
Most widely used tool (along LP) for decision making
Usually on a computer with appropriate software
An analysis (descriptive) tool
American College of Computer & Information Sciences
AI
NONE 201

Spring 2011
Chapter 2
Verify that the following functions are probability mass functions, and determine the
requested probabilities.
1.
a)
b)
c)
d)
Solution:
 All probabilities are greater than or equal to zero and sum to one.
a) = 1/8 + 2/8 + 2/8 + 2/8 + 1/8 = 1
b)