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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