Experiments:

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 back-propagation 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 neighbor 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.