Durgasoft SCJP Notes
Part-2
http:/javabynataraj.blogspot.com
1 of 401.
http:/javabynataraj.blogspot.com
2 of 401.
http:/javabynataraj.blogspot.com
3 of 401.
http:/javabynataraj.blogspot.com
4 of 401.
http:/javabynataraj.blogspot.com
5 of 401.
http:/javaby
Durgasoft SCJP Notes
Part-1
http:/javabynataraj.blogspot.com
1 of 255.
http:/javabynataraj.blogspot.com
2 of 255.
http:/javabynataraj.blogspot.com
3 of 255.
http:/javabynataraj.blogspot.com
4 of 255.
http:/javabynataraj.blogspot.com
5 of 255.
http:/javaby
Heaps & Priority Queues
(Walls & Mirrors - Remainder of Chapter 11)
1
Overview
Array-Based Representation of a Complete Binary Tree
Heaps
The ADT Priority Queue
Heap Implementation of the ADT Priority Queue
Heapsort
2
Complete Binary Tree
Recall tha
IBM PAPER1
1. In 1978, a kg of paper was sold at Rs25/-. If the paper rate increases at 1.5%
more than the inflation rate which is 6.5% a year, then what will be the cost of a kg
of paper after 2 years?
(a) 29.12
(b) 29.72
(c) 30.12
(d) 32.65
(e) none of
Introduction to Algorithms
6.046J/18.401J/SMA5503
Lecture 6
Prof. Erik Demaine
2001 by Charles E. Leiserson
to Algorithms
Introduction
Day 9
L6.1
Order statistics
Select the ith smallest of n elements (the
element with rank i).
Naive algorithm: Sort and i
Modern Adaptive Web Apps
A Dissertation
Submitted in partial fulfillment of the
requirement for
the award of the degree
of
MASTER OF COMPUTER APPLICATIONS
By
Vijay Pal
Under the Guidance of
Prof. Roshan Lal
Department of Mathematics
INDIAN INSTITUTE OF TE
Graph search
Depth-first search in undirected graphs
The purpose is to explore connectivity and reachability
The search uses backtracking
DFS progresses by expanding the first child node of the
starting node going deeper and deeper until hits a node
th
Introduction to Algorithms
6.046J/18.401J/SMA5503
Lecture 9
Prof. Charles E. Leiserson
2001 by Charles E. Leiserson
tion to Algorithms
Introduc
Day 17 L9.1
Binary-search-tree sort
Create an empty BST
for i = 1 to n
do TREE-INSERT (T, A[i])
Perform an ino
Graphs
A graph is a mathematical object that represents the
relationship between its elements
The elements are a set of vertices V
The relationships are a set of edges E of the type (v1, v2)
The edges can have weights attached to them
Graphs are dire
BLOOD BANK, IITR
Department of Mathematics
Guided
By
Dr. Roshan Lal
Submitted By:
Siddhant Kumar Kushwaha(13811034)
Vijay Pal(13811042)
Waseem Akram(13811045)
MCA 3rd Year
BLOOD BANK, IITR
Title of Project: Blood Bank, IITR
Objective:
This is the web appl
7.3 Kruskals Algorithm
Kruskals Algorithm was developed by
JOSEPH KRUSKAL
Kruskals Algorithm
Pick the cheapest link (edge) available
and mark it
Pick the next cheapest link available and
mark it again
Continue picking and marking link that
does not cr
Programming Assignment 1
/ Name : Abhishek Kumar Mishra
/ Roll : 11811002
/ Class: MCA 2nd year
Q-1:
/program to sort array pairwise
#include<iostream>
using namespace std;
int main()
cfw_
int n,i,j,k,p;
cout<"enter no of nuts and bolts\n";
cin>n;
n=2*n;
Friday, November 06, 2015
9:53 PM
General Page 1
General Page 2
General Page 3
General Page 4
General Page 5
General Page 6
General Page 7
General Page 8
General Page 9
General Page 10
Wednesday, November 04, 2015
10:33 AM
General Page 1
General Page 2
General Page 3
General Page 4
General Page 5
General Page 6
General Page 7
General Page 8
General Page 9
General Page 10
Problems in Forward chaining
Inference can explode forward and may never terminate.
Inference is not directed towards any particular conclusion or goal.
May draw lots of irrelevant conclusions.
Backward Chaining
Start from query or atomic sentence to be
9/9/2015
Game Playing
Game Playing and AI
Games are well-defined problems that are generally interpreted as
requiring intelligence to play well.
Search spaces can be very large.
For chess:
-Branching factor: 35
- Depth: 50 moves each player
-Search tree
Propositional
Logic
Logic in general
Logics are formal languages for representing information
such that conclusions can be drawn
Syntax defines the sentences in the language
Semantics define the "meaning" of sentences;
i.e., define truth of a sentence
Example: What is the next number in the sequence 6, 13, 20, 27,
There is more than one correct answer.
Inductive Reasoning, involves going from a series of specific cases
to a general statement. The conclusion in an inductive argument is
never guaranteed.
10/13/2015
PLANNING
Planning vs problem solving
Situation calculus
Plan-space planning
We studied how to take actions in the world
(search)
We studied how to represent objects, relations, etc.
(logic)
Now we will combine the two!
Problem solving
Logic rep
7/31/2015
Introduction to Artificial
Intelligence
The ability to solve problems
Search: Efficient trial and error
Enormous computational complexity
Space time trade offs
Use of domain knowledge heuristics
1
7/31/2015
The 8-queens problem can be define
Unsupervised
Learning
Unsupervised Learning
Supervised learning used labeled data pairs (x, y) to
learn a function f : XY.
But, what if we dont have labels?
No labels = unsupervised learning
Clustering is the unsupervised grouping of data
points. It c
8/14/2015
Artificial Intelligence
Solving problems by searching
Problem solving agent
Problem is simplified if an agent can adopt
a goal and aim at satisfying it.
1. Goal Formulation: Set of one or more
(desirable) world states (e.g. checkmate in
chess).
First Order Logic
Formal Languages and Commitments
Language
Ontological
Commitment
Epistemological
Commitment
(what is)
(what can be known)
Propositional Logic
facts
true, false, unknown
First-order Logic
facts, objects,
relations
true, false, unknown
Tem
Artificial Neural Network
Neurons in the Brain
Although heterogeneous, at a low level the
brain is composed of neurons
A neuron receives input from other neurons
(generally thousands) from its synapses
Inputs are approximately summed
When the input ex
9/4/2015
Constraint Satisfaction Problems
Message:
CSP propagation techniques can dramatically reduce search.
After placing the first queen, what would you do for the 2nd?
Reasoning, inference or propagation.
One thing that we can notice is that in, e.g.,
9/4/2015
Artificial Intelligence
Solving problems by searching
Informed Search
relies on additional knowledge about the problem or
domain
frequently expressed through heuristics (rules of
thumb)
used to distinguish more promising paths towards a goal
Learning
k Nearest-Neighbor Classification
Instance Based Classifiers
Lazy learning
No model is learned
The stored training instances themselves represent the knowledge
Training instances are searched for instance that most closely resembles
new insta