MACHINE LEARNING LABORATORY (15CSL76)

MACHINE LEARNING LABORATORY

NOTE:

1. The programs can be implemented in either JAVA or Python.
2. For Problems 1 to 6 and 10, programs are to be developed without using the built-in 

   classes  or APIs of Java/Python.
3. Data sets can be taken from standard repositories 

   (https://archive.ics.uci.edu/ml/datasets.html) or constructed by the students.

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