CS 524 Homework #3
Due: March 14, 2017
This homework contains both technical and business-related problems, for the total of 100 points. Note
that Problem 3 requires a good deal of a self-study. To this end, consider it a typical every-day problem
you wou
CS 513
Knowledge Discovery & Data
Mining
Hierarchical Clustering
k-Means Clustering
M. Daneshmand
CS 513 - Spring 2015
1
Clustering Task
Clustering refers to grouping records, observations, or
tasks into classes of similar objects
Cluster is collection
CS 524 A
Introduction to Cloud Computing
Lecture 7 Data Networking and Distributed Computation (Part 3)
Cloud pipes
OUTLINE
The business side: New Cloud service Use Cases and examples
A bit more detail on the Internet network layer
QoS
Packet Scheduling D
CS 524 A
Introduction to Cloud Computing
Lecture 5: Data Networking and Distributed
Computation (Part 1)
OUTLINE (CLOUD COMPUTING = VIRTUAL MACHINES +
VIRTUAL NETWORKS + ORCHESTRATION AND
MANAGEMENT)
Our topic is Networking
Two major topics
Introduction
CS 524 A
Introduction to Cloud Computing
Lecture 4: Virtual Machines Part II
OUTLINE
History and motivation
Mechanisms to achieve virtualization and the obstacles in
their ways
The Popek and Goldberg requirements
The hypervisor architecture
Non-virtua
Given,
K =2
Records:
a
b c
d
e f
g
h
I
j
(2,0) (1,2) (2,2) (3,2) (2,3) (3,3) (2,4) (3,4) (4,4) (3,5)
K-means clustering
Assigning 2 random values to the cluster
Cluster center = (2,0) , (3,5) i.e., a and j
Finding the nearest cluster center for each recor
A=imread('/Users/jaydangi/Downloads/cs558s17_hw1/plane.pgm');
X=double(A);
for i=1:size(X,1)-2
for j=1:size(X,2)-2
Cols=(2*X(i+2,j+1)+X(i+2,j)+X(i+2,j+2)-(2*X(i,j+1)+X(i,j)+X(i,j+2);
Rows=(2*X(i+1,j+2)+X(i,j+2)+X(i+2,j+2)-(2*X(i+1,j)+X(i,j)+X(i+2,j);
A(i,
SFO AIRPORT CUSTOMER SURVEY
DATA CLUSTERING DISCOVERING KNOWLEDGE IN
DATABASE, TERM PROJECT
Prof. M. Daneshmand,
Accomplished by: Mohsen Mosleh, Fall 2012
Topics
Problem statement (Business understanding Phase)
Data (Data Understanding)
Methodology (Data
Deriving Rules From Data
Deriving Rules from Data
Machine Learning Algorithms
Artificial Neural Network Algorithm
M. Daneshmand
November 29, 2015
MD-CS 513-Spring 2015
1
Neural Networks
Simulating the Brain to Solve Problems
Artificial Neural Networks (AN
The Prediction With Stocks Returns
An Application of Artificial Neural Network
Zichen Zhao
FE 590
Fall 2012
Prof. M. Daneshmand
Stevens Institute of Technolog
Portfolio
manageme
nt
Rate of return of
stocks
ANN model
Artificial Neural
Network
Background
FE 590 INTRODUCTION TO
KNOWLEDGE ENGINEERING
COURSE PROJECT:
S&P 500 INDEX PRICE LEVEL
PREDICTION
BY CHUHAN LIN
Prof. Mahmoud Daneshmand
Financial Engineering
School of Systems and Enterprises
Stevens Institute of Technology
Fall 2012
1
OUTLINE
Introducti
CS 524 A
Introduction to Cloud Computing
Lecture 6: Data Networking and Distributed Computation
(Part 2)
How the Cloud pipes are made
OUTLINE
The business side: New Cloud service Use Cases
and examples
A bit more detail on the Internet network layer
Subne
NAME : JAY DANGI
CWID : 10421655
LAB ASSIGNMENT #1
Steps to create AWS account
1) Open https:/aws.amazon.com/ec2/ in Chrome.
2) Click on Sign in to Console
3) I am a new user. So I Signup in AWS.
4) Select new password and create AWS account.
5) After tha
1
CS 558:
Computer Vision
2nd Set of Notes
Instructor: Enrique Dunn
Webpage: www.cs.stevens.edu/~edunn
E-mail: [email protected]
Office: Lieb 310
Slide Credits
This set of sides also contains contributions
kindly made available by the following
authors
Homework
1
Homework 1.1
True or False?
ABAB
(A B) C A (B C)
(A B) C A (B C)
A ( A B) A
A B C (A B) C
2
Homework 1.2
Juan is playing the following game: he rolls two dice. If they sum up
to 7 he loses a dollar. If they sum up to 2, he wins 2 dollars.
Othe
CS 513: Knowledge Discovery in Databases
Stevens Institute of Technology
Instructor
Khasha Dehnad
[email protected][email protected]
Teaching Assistant
TBD
Spring 2013
1
Course Requirements
Prerequisites:
Familiarity with the principals of sta
Stevens Institute of Technology
Khasha Dehnad
Spring 2013
1
R and R-Studio - - Download
Spring 2013
2
R and R Studio Download
in Databases I
http:/www.r-project.org/
http:/www.rstudio.com/ide/download/
3
Intro to R: R-Studio
4
MDMIS
637
Sum
mer
Intro to R
Agenda
in Databases I
Housekeeping
Lecture 1 :
Intro to data Mining
Intro to Probability
R down loads
1
Definitions
Data
Information
Data with Relevance and Importance
Data that changes the probability of a relevant outcome.
Knowledge
Representations
Review
Probability
1
Events
Definition: any collection of outcomes of an experiment.
Events consisting of single outcomes in the sample space
are called elementary or simple events.
Events consisting of more than one outcome are called
compound events.
CS 513
Knowledge Discovery & Data Mining
k-Nearest Neighbor Algorithm
Khasha Dehnad
1
Supervised vs. Unsupervised
Methods
Data mining methods are categorized as either
Unsupervised or Supervised
Unsupervised Methods
A target variable is not specified
Name: Jay Dangi
Homework #1
CS558- Computer Vision
CWID: 10421655
1.Gaussianfilteringoftheinputimage.AllowtheusertospecifytheoftheGau
ssianfunction (do not forget the normalizing constant). Make sure
that the filter size is large enough and that the filte
1
CS 558:
Computer Vision
5th Set of Notes
Instructor: Enrique Dunn
Webpage: www.cs.stevens.edu/~edunn
E-mail: [email protected]
Office: Lieb 310
Overview
Hough Transform
Template Matching
Image Alignment
Based on slides by S. Lazebnik, K. Grauman
and
1
CS 558:
Computer Vision
3rd Set of Notes
Instructor: Enrique Dunn
Webpage: www.cs.stevens.edu/~edunn
E-mail: [email protected]
Office: Lieb 310
Overview
Filtering and Denoising
Based on slides by S. Lazebnik
Edge detection
Based on slides by S. Laze
1
CS 558:
Computer Vision
6th Set of Notes
Instructor: Enrique Dunn
Webpage: www.cs.stevens.edu/~edunn
E-mail: [email protected]
Office: Lieb 310
Template Matching
Slides based on D. Hoiems slides
Template matching
Goal: find
in image
Main challenge: Wh
p(j/tl)
Split
Savings =Low
PL
3/8
0.375
PR
5/8
0.625
Good
1/3
0.333
Bad
2/3
0.667
Savings =Med
Savings=High
Q ( s / t ) 2 P L * P R
p(j/tl)
Split
Assets=Low
Assets=Med
Assets=High
PL
PR
Good
Bad
0.250
0.750
0.000
1.000
p(j/tr)
Good
Bad
4/5
1/5
0.800
0.20