AL3411: Artificial Intelligence and Machine Learning Laboratory syllabus for AI&ML 2021 regulation

Artificial Intelligence and Machine Learning Laboratory detailed syllabus for Artificial Intelligence & Machine Learning (AI&ML) for 2021 regulation curriculum has been taken from the Anna University official website and presented for the AI&ML 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 & Machine Learning 4th Sem scheme and its subjects, do visit AI&ML 4th Sem 2021 regulation scheme. The detailed syllabus of artificial intelligence and machine learning laboratory is as follows.

Course Objectives:

The main objectives of this course are to:

• To learn to implement uninformed and informed search techniques.
• To build a knowledge base in Prolog and process queries to perform inference.
• To build supervised learning models.
• To explore the regression models.
• To learn to compare and evaluate the performance of different models

List of Experiments:

1. BFS & DFS algorithm implementation
2. A* algorithm implementation
3. Hill Climbing implementation
4. Develop a small KB using Prolog and answer simple queries
5. Inference through Prolog/Python
6. Write a program to implement the naïve Bayesian classifier for credit card analysis and compute the accuracy with a few test data sets.
7. 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.
8. Build an Artificial Neural Network by implementing the Backpropagation algorithm and test the same using appropriate data sets.
9. Evaluate the performance of Linear regression, logistic regression, naïve Bayes and SVM based prediction models for heart disease diagnosis.

List of Equipments:

1. Tools: Python, Numpy, Scipy, Matplotlib, Pandas, statmodels, seaborn, plotly, bokeh
2. Note: Example data sets like: UCI, Iris, Pima Indians Diabetes etc.

Course Outcomes:

1. Implement uninformed and informed search techniques
2. Build a knowledge base in Prolog and process queries to perform inference
3. Develop supervised learning models
4. Develop regression models
5. Compare and evaluate the performance of different models

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

For all Artificial Intelligence & Machine Learning results, visit Anna University AI&ML all semester results direct link.