# AL3461 MACHINE LEARNING LABORATORY Anna University Syllabus R2021

## AL3461 MACHINE LEARNING LABORATORY L T P C0042

OBJECTIVES:

- To understand the data sets and apply suitable algorithms for selecting the appropriate features for analysis.

- To learn to implement supervised machine learning algorithms on standard datasets and evaluate the performance.

- To experiment the unsupervised machine learning algorithms on standard datasets and evaluate the performance.

- To build the graph based learning models for standard data sets.

- To compare the performance of different ML algorithms and select the suitable one based on the application.

LIST OF EXPERIMENTS:

1. 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.

2. 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.

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

4. Write a program to implement the naïve Bayesian classifier for a sample training data set stored as a .CSV file and compute the accuracy with a few test data sets.

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

6. Write a program to construct a Bayesian network to diagnose CORONA infection using standard WHO Data Set.

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

8. Write a program to implement k-Nearest Neighbour algorithm to classify the iris data set. Print both correct and wrong predictions.

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

List of Equipments:(30 Students per Batch)

The programs can be implemented in either Python or R.

TOTAL:60 PERIODS

OUTCOMES:

At the end of this course, the students will be able to:

CO1:Apply suitable algorithms for selecting the appropriate features for analysis.

CO2:Implement supervised machine learning algorithms on standard datasets and evaluate the performance.

CO3:Apply unsupervised machine learning algorithms on standard datasets and evaluate the performance.

CO4:Build the graph based learning models for standard data sets.

CO5:Assess and compare the performance of different ML algorithms and select the suitable one based on the application

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