AL3411 Artificial Intelligence and Machine Learning Laboratory Syllabus:

AL3411 Artificial Intelligence and Machine Learning Laboratory Syllabus – Anna University Regulation 2021

COURSE OBJECTIVES:

 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:(30 Students per Batch)
Tools: Python, Numpy, Scipy, Matplotlib, Pandas, statmodels, seaborn, plotly, bokeh
Note: Example data sets like: UCI, Iris, Pima Indians Diabetes etc.

TOTAL: 60 PERIODS

COURSE OUTCOMES:

At the end of this course, the students will be able to:
CO1: Implement uninformed and informed search techniques
CO2: Build a knowledge base in Prolog and process queries to perform inference
CO3: Develop supervised learning models
CO4: Develop regression models
CO5: Compare and evaluate the performance of different models