AL3461: Machine Learning Laboratory syllabus for AI&DS 2021 regulation

Machine Learning Laboratory detailed syllabus for Artificial Intelligence & Data Science (AI&DS) for 2021 regulation curriculum has been taken from the Anna University official website and presented for the AI&DS students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below.

For Artificial Intelligence & Data Science 4th Sem scheme and its subjects, do visit AI&DS 4th Sem 2021 regulation scheme. The detailed syllabus of machine learning laboratory is as follows.

Course 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 naive 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 naive 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.

Course Outcomes:

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

1. Apply suitable algorithms for selecting the appropriate features for analysis.
2. Implement supervised machine learning algorithms on standard datasets and evaluate the performance.
3. Apply unsupervised machine learning algorithms on standard datasets and evaluate the performance.
4. Build the graph based learning models for standard data sets.
5. Assess and compare the performance of different ML algorithms and select the suitable one based on the application.

For detailed syllabus of all other subjects of Artificial Intelligence & Data Science, 2021 regulation curriculum do visit AI&DS 4th Sem subject syllabuses for 2021 regulation.

For all Artificial Intelligence & Data Science results, visit Anna University AI&DS all semester results direct link.