https://github.com/singhpratyush/intro-to-ai
Small Code Samples for Intro to AI Lab
https://github.com/singhpratyush/intro-to-ai
Last synced: 8 months ago
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Small Code Samples for Intro to AI Lab
- Host: GitHub
- URL: https://github.com/singhpratyush/intro-to-ai
- Owner: singhpratyush
- Created: 2017-04-19T15:09:44.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2017-04-19T19:05:28.000Z (over 8 years ago)
- Last Synced: 2025-01-26T16:35:02.660Z (9 months ago)
- Language: Python
- Size: 229 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Intro to AI Lab Codes
#### Requirements
```bash
pip install -r requirements.txt
```
#### Usage
```bash
python lab/.py
```
### Lab 1. Linear Perceptron for Binary Classification
* Perform binary classification on the iris dataset with two classes using linear perceptron.
* Use different training/testing sized to see the effectiveness of algorithm.
* Use pocket algorithm to get optimum weights.
### Lab 2. Non Linear Transforms in Binary Classification
* Perform binary classification on wine quality dataset
* Use Non-Linear combination of degree two and compare results.
### Lab 3. Linear Regression using Perceptron
* Perform linear regression for quality of wine on wine quality dataset.
* Check error for different training and testing sizes.
* Use weights of regression to perform classification.
### Lab 4. Logistic Regression using Perceptron
* Perform logistic regression on wine quality dataset to predict classes.
* Check performance for different data sizes.
* Calculate hard and soft errors.
### Lab 5. Graph Algorithms
* Implement these graph traversal algorithms -
* Depth First Search
* Breadth First Search
* A* Search
* Find solutions to following problems -
* 8 Number Problem
* N-Queen Problem