Printed Pages :
| ECS601
(Following Paper 1]) and Roll No. to be lled in yourAnswer Book)
PAPER ID : 110601
B. Tech.
(SEM. VI) THEORY EXAM[NATION, 2014-15
COMPUTER NETWORK
Time : 3 Hours] [Total Marks : 100
NOTE: ATTEMPT ALL QUESTIONS.
1 ATTEMPT ANY FOU
Data Compression (ECS-077)
B.TECH
(SEM. 7) (ODD SEM) THEORY EXAMINATION, 2015-16
Data Compression
Code: (ECS-077)
Time: 3 Hours
[Total Marks: 100]
Section-A
Q.1 Attempt all parts. All parts carry equal marks. Write answer of each part in short.
(2x10 = 20
ECS601
Printed Pages3
(Following Paper ID and Roll No. to be filled in your Answer Book)
PAPER ID : 110601 Roll No.
B.Tech.
(SEM. VI) THEORY EXAMINATION 2013-14
COMPUTER NETWORK
Time : 3 Hours
Note : (1)
(2)
1.
Total Marks : 100
Attempt all questions.
All
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w
.c
om
ce
ra
am
.e
x
w
P[P~a]~t.,.E[t'J.t >I
and verify the inequality
P[ X~ 2A] ~ (e14/
when X follows Poisson distribution with
parameter.
20
(b)
Let Fn be a sequence of distribution functions defined by
0,
X
<0
~
t
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om
F,.(x)= 1-, x~x<n
x~n
I
Does
Home Work 1
Due Date: 12th Sep 2010
(Attempt all questions)
Q. 1] Let k k denote the standard Euclidean norm on R2 . For each of the following
subsets of R2 say whether it is closed / open / neither. Also identify the sets which
are convex / bounded. For
Improved bounds for the randomized decision tree complexity of recursive
majority
Frederic Magniez
Ashwin Nayak
Miklos Santha
David Xiao
Abstract
We consider the randomized decision tree complexity of the recursive 3-majority function. For evaluating
a he
Problem Set 2
Design and Analysis of Algorithms
August 29, 2016
Please submit your solutions to [email protected] by 12 pm on September 5.
1. Let A(x) and B(x) be two univariate polynomials of degree n. Assume that the coefficients of A and
B are smal
E0 270 Machine Learning
Lecture 3 (Jan 15, 2013)
Discriminative Probabilistic Models for Classification
Lecturer: Shivani Agarwal
Disclaimer: These notes are a brief summary of the topics covered in the lecture.
They are not a substitute for the full lect
E0 270 Machine Learning
Lecture 4 (Jan 17, 2013)
Least Squares Regression
Lecturer: Shivani Agarwal
Disclaimer: These notes are a brief summary of the topics covered in the lecture.
They are not a substitute for the full lecture.
Outline
Regression and c
E0 370 Statistical Learning Theory
Lecture 3 (Aug 16, 2011)
Uniform Convergence and Growth Function/VC-Entropy
Lecturer: Shivani Agarwal
1
Scribe: Shivani Agarwal
Introduction
In the previous lecture we reviewed the SVM learning algorithm, which when give
A Probabilistic Interpretation of
Canonical Correlation Analysis
Francis R. Bach
Computer Science Division
University of California
Berkeley, CA 94114, USA
[email protected]
Michael I. Jordan
Computer Science Division
and Department of Statistics
Univ
E0 370 Statistical Learning Theory
Lecture 15 (Oct 20, 2011)
Boosting
Lecturer: Shivani Agarwal
1
Scribe: Anil C R
Introduction
We start by discussing some variants of the learnability model we saw in the last few lectures, and consider
whether efficient
E0 270 Machine Learning
Lecture 24 (Apr 4, 2013)
A Glimpse into Statistical Learning Theory: Statistical Consistency of
Binary Classification Algorithms Based on Risk Minimization
Lecturer: Shivani Agarwal
Disclaimer: These notes are a brief summary of th
E0 370 Statistical Learning Theory
Lecture 4 (Aug 23, 2011)
VC-Dimension and Sauers Lemma
Lecturer: Shivani Agarwal
1
Scribe: Achintya Kundu
Introduction
In the previous lecture we saw the technique of uniform convergence for obtaining confidence bounds o
E0 270 Machine Learning
Lecture 1 (Jan 8, 2013)
Introduction. Binary Classification and Bayes Error.
Lecturer: Shivani Agarwal
Disclaimer: These notes are a brief summary of the topics covered in the lecture.
They are not a substitute for the full lecture
E0 270 Machine Learning
Lecture 2 (Jan 10, 2013)
Generative Probabilistic Models for Classification
Lecturer: Shivani Agarwal
Disclaimer: These notes are a brief summary of the topics covered in the lecture.
They are not a substitute for the full lecture.
E0 270 Machine Learning
Lecture 5 (Jan 22, 2013)
Support Vector Machines for Classification and Regression
Lecturer: Shivani Agarwal
Disclaimer: These notes are a brief summary of the topics covered in the lecture.
They are not a substitute for the full l
E0 270 Machine Learning
Lecture 10 (Feb 7, 2013)
Canonical Correlation Analysis
Lecturer: Chiranjib Bhattacharyya
Scribe: Debarghya Ghoshdastidar
Disclaimer: These notes are a brief summary of the topics covered in the lecture.
They are not a substitute f
E0 271: Computer Graphics
Assignment 2 (Weightage: 20%)
Due on Sep 22, 2016
In this assignment, you will add more features to the protein viewer you built in the previous assignment. However, now you have to use new protein data which is available at http
E0 271: Computer Graphics
Assignment 1
Due: August 25, 2016
Weightage: 15%
Goals
Learn OpenGL
Learn basic GLSL
Learn to use GLUT/Qt
Analyze graphics performance
You need to implement a simple protein viewer in OpenGL and GLUT (or Qt). You will add mor
Problem Set 5
Design and Analysis of Algorithms
September 30, 2016
Please submit your solutions to [email protected] by noon 12 pm (IST) on October 10.
Problem 1. You want to find a straight line ax + by = c that approximates the function y = x2 in th
Homework Assignment 2
Design and Analysis of Algorithms
September 30, 2016
Please submit your solutions to [email protected] by noon 12 pm (IST) on October 21.
Problem 1. Given a matrix A Rmn and a vector b Rm , consider the polyhedron P := cfw_x Rn |
Problem Set 4
Design and Analysis of Algorithms
September 26, 2016
Please submit your solutions to [email protected] by 12 pm on October 3.
1. Recall that a standard queue maintains a sequence of items and supports the following operations:
Push(x) (a
Homework Assignment 1
Design and Analysis of Algorithms
September 6, 2016
Please submit your solutions to [email protected] by 12 pm on September 20.
Problem 1. Show that in any execution of the Gale-Shapley stable matching algorithm, the last woman t