There is No Full Stop for Learning !!

The programs can be implemented in either JAVA or Python.
For Problems 1 to 6 and 10, programs are to be developed without using the built-in classes or APIs of Java/Python.
Data sets can be taken from standard repositories (https://archive.ics.uci.edu/ml/datasets.html) or constructed by the students.

Implement and demonstrate the FIND-S algorithm for finding the most specific hypothesis based on a given set of training data samples. Read the training data from a .CSV file.
For a given set of training data examples stored in a .CSV file, implement and
demonstrate the Candidate-Elimination algorithm to output a description of the set

of all hypotheses consistent with the training examples.
Write a program to demonstrate the working of the decision tree based ID3 algorithm. Use an appropriate data set for building the decision tree and apply this knowledge to classify a new sample.
Build an Artificial Neural Network by implementing the Backpropagation algorithm and test the same using appropriate data sets.
Write a program to implement the naïve Bayesian classifier for a sample training
data set stored as a .CSV file. Compute the accuracy of the classifier, considering few

test data sets.
Assuming a set of documents that need to be classified, use the naïve Bayesian
Classifier model to perform this task. Built-in Java classes/API can be used to write

the program. Calculate the accuracy, precision, and recall for your data set.
Write a program to construct a Bayesian network considering medical data. Use this

model to demonstrate the diagnosis of heart patients using standard Heart Disease

Data Set. You can use Java/Python ML library classes/API.
Apply EM algorithm to cluster a set of data stored in a .CSV file. Use the same data
set for clustering using k-Means algorithm. Compare the results of these two

algorithms and comment on the quality of clustering. You can add Java/Python ML

library classes/API in the program.
Write a program to implement k-Nearest Neighbour algorithm to classify the iris

data set. Print both correct and wrong predictions. Java/Python ML library classes can

be used for this problem.
Implement the non-parametric Locally Weighted Regression algorithm in order to
fit data points. Select appropriate data set for your experiment and draw graphs.

MACHINE LEARNING Syllabus (15CS73)

Subject Code: 15CS73 Semester: 7 Scheme: CBCS

Text Books:

Tom M. Mitchell, Machine Learning, India Edition 2013, McGraw Hill Education

Reference Books:

Trevor Hastie, Robert Tibshirani, Jerome Friedman, h The Elements of Statistical Learning, 2nd edition, springer series in statistics.
Ethem Alpaydın, Introduction to machine learning, second edition, MIT press.

Module – 1: Text Book1, Sections: 1.1 – 1.3, 2.1-2.5, 2.7

Introduction:

Well posed learning problems
Designing a Learning system
Perspective and Issues in Machine Learning.

Concept Learning:

Concept learning task
Concept learning as search
Find-S algorithm
Version space
Candidate Elimination algorithm
Inductive Bias

Module – 2: Text Book1, Sections: 3.1-3.7

Decision Tree Learning:

Decision tree representation
Appropriate problems for decision tree learning
Basic decision tree learning algorithm
hypothesis space search in decision tree learning
Inductive bias in decision tree learning
Issues in decision tree learning

Module – 3: Text book 1, Sections: 4.1 – 4.6

Artificial Neural Networks:

Introduction
Neural Network representation
Appropriate problems
Perceptrons
Backpropagation algorithm

Module – 4: Text book 1, Sections: 6.1 – 6.6, 6.9, 6.11, 6.12

Bayesian Learning:

Introduction
Bayes theorem
Bayes theorem and concept learning
ML and LS error hypothesis
ML for predicting probabilities
MDL principle
Naive Bayes classifier
Bayesian belief networks
EM algorithm

Module – 5: Text book 1, Sections: 5.1-5.6, 8.1-8.5, 13.1-13.3

Evaluating Hypothesis:

Motivation
Estimating hypothesis accuracy
Basics of sampling theorem
General approach for deriving confidence intervals
Difference in error of two hypothesis
Comparing learning algorithms

Instance Based Learning:

Introduction
k-nearest neighbor learning
locally weighted regression
radial basis function cased-based reasoning

Reinforcement Learning:

Introduction
Learning Task
Q Learning

Tuesday, 6 February 2018

Python Application Programming: 15CS664 (VTU 6th sem)

For notes please click on specific links

For modules 1,2,3,5 refer to this text: click here
For module 4 refer to this text: click here

Module 1

Module 2:

Module 3:

Module 4:

T2, Chapter 15: Classes and objects
T2, Chapter 16: Classes and functions
T2, Chapter 17: Classes and methods

Module 5: PPT

T1, Chapter 12: Networked programs
T1, Chapter 13: Using Web Services
T1, Chapter 15: Using databases and SQL

credits: Wikipedia, Tutorialpoint

Friday, 21 April 2017

Program to check whether string is palindrome or not without using library function

#include<stdio.h>

void main()

{

char s[20];

int i=0, len, flag = 0;

printf("\nEnter s1: ");

gets(s);

/*To find the length of the string*/

while(s[i]!='\0')

{

i++;

}

len = i;

for(i=0; i<len/2; i++)

{

if(s[i] != s[len-i-1])

{

flag = 1;

break;

}

if(flag == 0)

printf("\nString is palindrome.");

else

printf("\nString is not palindrome.");

}

Output:

Enter s1: abcba

Strings is palindrome.

Program to check whether string is palindrome or not

#include<stdio.h>
#include<string.h>

void main()
{
char s1[20], s2[20];
printf("\nEnter s1: ");
gets(s1);

/*Copy s1 contents to s2*/
strcpy(s2, s1);

/*Reverse s2*/
strrev(s2);

/*Compare s1 and s2*/
if(strcmp(s1, s2) == 0)
printf("\nString is palindrome.");
else
printf("\nString is not a palindrome");

}

Output:
case 1:
Enter s1: abcde
String is not a palindrome

case 2:
Enter s1: abcba
String is palindrome.

Program to compare two strings without using library function strcmp

#include<stdio.h>

#include<string.h>

void main()

{

int len1, len2, flag = 0, i;

char s1[20], s2[20];

printf("\nEnter s1: ");

gets(s1);

printf("\nEnter s2: ");

gets(s2);

len1 = strlen(s1);

len2 = strlen(s2);

if(len1 != len2)

{

flag = 1;

}

else

{

for(i=0;i<len1;i++)

{

if(s1[i] != s2[i])

{

flag = 1;

break;

}

if(flag == 0)

printf("\nStrings are same.");

else

printf("\nStrings are different.");

}

Output:

Case 1:

Enter s1: abcde

Enter s2: abcdef

Strings are different.

Case 2:

Enter s1: abcde

Enter s2: abfgh

Strings are different.

Case 3:

Enter s1: abcde

Enter s2: abcde

Strings are same.

Sunday, 16 April 2017

Program for arithmetic calculator using switch

Program to demo arithmetic calculator using switch

#include<stdio.h>
void main()
{
int a, b;
float res;
char op;
printf("\nEnter a op b :");
scanf("%d %c %d", &a, &op, &b);
switch(op)
{
case '+' : res = a+b;
break;
case '-' : res = a-b;
break;
case '*' : res = a * b;
break;
case '/' : if(!b)
printf("\n Divide by zero error");
else
res = (float)a/b;
break;
case '%' : res = a % b;
break;
default: printf("\nGive valid operator+");
break;
}
printf("\n Result = %f", res);
}

Output:

Enter a op b : 1 / 2
Result = 0.500000

Monday, 1 April 2019

Wednesday, 13 February 2019

Friday, 27 July 2018

Lab Program 10:

Implement the non-parametric Locally Weighted Regression algorithm in order to fit data points. Select appropriate data set for your experiment and draw graphs.

Lab Program 9:

Write a program to implement k-Nearest Neighbour algorithm to classify the iris data set. Print both correct and wrong predictions. Java/Python ML library classes can be used for this problem.

Lab Program 8:

Apply EM algorithm to cluster a set of data stored in a .CSV file. Use the same data set for clustering using k-Means algorithm. Compare the results of these two algorithms and comment on the quality of clustering. You can add Java/Python ML library classes/API in the program.

Lab Program 7:

Write a program to construct a Bayesian network considering medical data. Use this model to demonstrate the diagnosis of heart patients using standard Heart Disease Data Set. You can use Java/Python ML library classes/API.

Lab Program 6:

Assuming a set of documents that need to be classified, use the naïve Bayesian Classifier model to perform this task. Built-in Java classes/API can be used to write the program. Calculate the accuracy, precision, and recall for your data set.

Lab Program 5:

Write a program to implement the naïve Bayesian classifier for a sample training data set stored as a .CSV file. Compute the accuracy of the classifier, considering few test data sets.

Lab Program 4:

Build an Artificial Neural Network by implementing the Backpropagation algorithm and test the same using appropriate data sets.

Lab Program 3:

Write a program to demonstrate the working of the decision tree based ID3 algorithm. Use an appropriate data set for building the decision tree and apply this knowledge to classify a new sample.

Lab Program 2:

For a given set of training data examples stored in a .CSV file, implement and demonstrate the Candidate-Elimination algorithm to output a description of the set of all hypotheses consistent with the training examples.

Lab Program 1:

Implement and demonstrate the FIND-S algorithm for finding the most specific hypothesis based on a given set of training data samples. Read the training data from a .CSV file.

MACHINE LEARNING LABORATORY

MACHINE LEARNING Syllabus (15CS73)

Tuesday, 6 February 2018

Friday, 21 April 2017

Program to check whether string is palindrome or not without using library function

Program to check whether string is palindrome or not

Program to compare two strings without using library function strcmp

Sunday, 16 April 2017

Program to demo arithmetic calculator using switch

Implement the non-parametric Locally Weighted Regression algorithm in order to
fit data points. Select appropriate data set for your experiment and draw graphs.

Write a program to implement k-Nearest Neighbour algorithm to classify the iris

data set. Print both correct and wrong predictions. Java/Python ML library classes can

be used for this problem.

Apply EM algorithm to cluster a set of data stored in a .CSV file. Use the same data
set for clustering using k-Means algorithm. Compare the results of these two

algorithms and comment on the quality of clustering. You can add Java/Python ML

library classes/API in the program.

Assuming a set of documents that need to be classified, use the naïve Bayesian
Classifier model to perform this task. Built-in Java classes/API can be used to write

the program. Calculate the accuracy, precision, and recall for your data set.

**Build an Artificial Neural Network by implementing the Backpropagation algorithm and test the same using appropriate data sets.**

**Write a program to demonstrate the working of the decision tree based ID3 algorithm. Use an appropriate data set for building the decision tree and apply this knowledge to classify a new sample.**

For a given set of training data examples stored in a .CSV file, implement and
demonstrate
the Candidate-Elimination algorithm to output a description of the set

of all hypotheses consistent with the training examples.