Benefits of Business Mobility
Enhance mobility
Provides immediate data access
Increases location and monitoring capability
Improves work flow
Provides mobile business opportunities
Provides alternativ
Q1. Bankers Algorithm
(a) [10 points] Briey describe two drawbacks of the Bankers algorithm for deadlock
avoidance.
1. Users need to estimate their maximum need; ifthese estimates are too high, the al
Lecture 20
Sublinear-Time
lgorithms
Supplemental reading in CLRS: None
If we settle for approximations, we can sometimes get much more efcient algorithms. In Lecture 18,
we saw polynomial-time approxi
Massachusetts Institute of Technology
6.867 Machine Learning Fall 2006
Problem Set 1 Solutions
Section A (background questions)
1. Lets begin with a little math. Let us denote by Pn the probability th
Lecture 23
Computational geometry
Supplemental reading in CLRS: Chapter 33 except 33.3
There are many important problems in which the relationships we wish to analyze have geometric
structure. For exa
Lecture 22
Derandomization
Supplemental reading in CLRS: None
Here are three facts about randomness:
Randomness can speed up algorithms (e.g., sublinear-time approximations)
Under reasonable assumpt
Massachusetts Institute of Technology
6.867 Machine Learning Fall 2006
Problem Set : Solutions
1. (a) (5 points) From the lecture notes (Eqn 14, Lecture 5), the optimal parameter values for linear
reg
Lecture 19
Compression and Huffman Coding
Supplemental reading in CLRS: Section 16.3
19.1
Compression
As you probably know at this point in your career, compression is a tool used to facilitate storin
Due April 13, 2012
6.046J/18.410J
Quiz 2
Design and Analysis of Algorithms
Massachusetts Institute of Technology
Profs. Dana Moshkovitz and Bruce Tidor
Quiz 2
35
30
25
20
15
10
5
0
(10, 20] (20, 30] (
Lecture 21
Clustering
Supplemental reading in CLRS: None
Clustering is the process of grouping objects based on similarity as quantied by a metric. Each
object should be similar to the other objects i
Design and Analysis of Algorithms
Massachusetts Institute of Technology
Profs. Dana Moshkovitz and Bruce Tidor
Final Exam
Mean: 121.3; Median: 122; Standard deviation: 30.8
May 23, 2012
6.046J/18.410J
Lecture 17
Complexity and NP-completeness
Supplemental reading in CLRS: Chapter 34
As an engineer or computer scientist, it is important not only to be able to solve problems, but also to
know which p
Problem 2
2.1. (3 points) Let F be a set of classiers whose VC-dimension is 5. Suppose we have
four training examples and labels, cfw_(x1 , y1 ), . . . , (x4 , y4 ), and select a classier f
from F by
Theory of Parallel Hardware
Massachusetts Institute of Technology
Charles Leiserson, Michael Bender, Bradley Kuszmaul
April 12, 2004
6.896
Handout 14
Solution Set 6
Due: In class on Wednesday, March 3
Design and Analysis of Algorithms
Massachusetts Institute of Technology
Profs. Dana Moshkovitz and Bruce Tidor
6.046J/18.410J
Practice Quiz 2 for Spring 2012
Practice Quiz 2 for Spring 2012
These prob
6.867 Machine learning
Mid-term exam
October 15, 2003
2 points) Your name and MIT ID:
SOLUTIONS
Problem 1
Suppose we are trying to solve an active learning problem, where the possible inputs you
can s
Lecture 18
Polynomial-Time
pproximations
Supplemental reading in CLRS: Chapter 35 except 35.4
If you try to design algorithms in the real world, it is inevitable that you will come across NP-hard
prob
Today
Proof of Converse Coding Theorem
Intuition: For message m, let Sm cfw_0, 1n
be the set of received words that decode to
m. (Sm = D1(m).
More on Shannons theory
Proof of converse.
Few words o
Theory of Parallel Hardware
Massachusetts Institute of Technology
Charles Leiserson, Michael Bender, Bradley Kuszmaul
April 23, 2004
6.896
Handout 16
Solution Set 7
Due: In class on Wednesday, April 7