AI and ML Application Development Laboratory (18AIL76)

This repository contains programs and experiments for the AI and ML Application Development Laboratory course (18AIL76). Below are the details of each task and instructions on how to run the programs:

Task 1: k-Nearest Neighbour Algorithm for Iris Classification

Program (Python): p1.py
Program (Jupyter Notebook): p1.ipynb
Description: This program implements the k-Nearest Neighbour algorithm to classify the Iris dataset. It prints both correct and wrong predictions.
How to Run: Execute the p1.py program using Python or open and run p1.ipynb using Jupyter Notebook.

Task 2: K-means and EM Clustering Comparison

Program (Python - K-means): p2_kmeans.py
Program (Jupyter Notebook - K-means): p2_kmeans.ipynb
Program (Python - EM): p2_em.py
Program (Jupyter Notebook - EM): p2_em.ipynb
Description: These programs apply the K-means and Expectation-Maximization (EM) algorithms to cluster a dataset stored in a .CSV file. Compare the results of these two algorithms and comment on the quality of clustering.
How to Run: Execute p2_kmeans.py and p2_em.py for K-means and EM clustering in Python, or open and run the corresponding Jupyter Notebook files.

Task 3: Locally Weighted Regression

Program (Python): p3.py
Program (Jupyter Notebook): p3.ipynb
Description: This program implements the non-parametric Locally Weighted Regression algorithm to fit data points. It selects an appropriate dataset for the experiment and draws graphs.
How to Run: Execute the p3.py program using Python or open and run p3.ipynb using Jupyter Notebook.

Task 4: Artificial Neural Network with Backpropagation

Program (Python): p4.py
Program (Jupyter Notebook): p4.ipynb
Description: This program builds an Artificial Neural Network (ANN) using the Backpropagation algorithm and tests it using appropriate datasets.
How to Run: Execute the p4.py program using Python or open and run p4.ipynb using Jupyter Notebook.

Task 5: Genetic Algorithm

Program (Python): p5.py
Program (Jupyter Notebook): p5.ipynb
Description: This program demonstrates the Genetic Algorithm by applying it to a suitable dataset for a simple application.
How to Run: Execute the p5.py program using Python or open and run p5.ipynb using Jupyter Notebook.

Task 6: Q Learning Algorithm

Program (Python): p6.py
Program (Jupyter Notebook): p6.ipynb
Description: This program demonstrates the Q Learning algorithm with suitable assumptions for a problem statement.
How to Run: Execute the p6.py program using Python or open and run p6.ipynb using Jupyter Notebook.

Feel free to explore and run these programs to understand various AI and ML algorithms and their applications.

For any questions or issues, please contact the course instructor or teaching assistant.

anikethhebbar / aiml