LIST OF EXPERIMENTS:

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

2. Write a program to demonstrate the working of the decision tree-based ID3 algorithm. Utilize an appropriate dataset for building the decision tree and apply it to classify a new sample.

3. Build an Artificial Neural Network by implementing the Backpropagation algorithm and test it using appropriate datasets.

4. Implement the naive Bayesian classifier for a sample training dataset stored as a .CSV file and compute accuracy with a few test datasets.

5. Utilize the naive Bayesian Classifier model to classify a set of documents and measure accuracy, precision, and recall.

6. Construct a Bayesian network to diagnose CORONA infection using the standard WHO dataset.

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

8. Implement the k-Nearest Neighbor algorithm to classify the iris dataset. Print both correct and incorrect predictions.

9. Implement the non-parametric Locally Weighted Regression algorithm to fit data points. Select an appropriate dataset for the experiment and draw graphs.