Intro to AI Final Project

This repository contains my final project for an Intro to Artificial Intelligence course.

Project Overview

The project covers three core supervised learning tasks and one bonus task:

1. Regression Task: Fuel Efficiency Prediction

   • Dataset: Auto MPG
   • Models: Linear Regression, Polynomial Regression, KNN Regression
   • Focus: exploratory data analysis, preprocessing, model comparison, optimization behavior, and validation-based model selection

2. Classification Task: Classical Models on CIFAR-10

   • Dataset: CIFAR-10
   • Models: Multiclass Logistic Regression, Linear SVM, KNN
   • Focus: hyperparameter tuning, validation accuracy, confusion matrices, and limitations of classical models on image data

3. Neural Network Classification with PyTorch

   • Dataset: CIFAR-10
   • Focus: architecture design, optimizer selection, hyperparameter search, validation-based model selection, and final test evaluation

4. Bonus Task: Neural Network Regression with PyTorch

   • Dataset: Auto MPG
   • Focus: adapting the PyTorch pipeline from classification to regression, selecting the best configuration using validation data, and comparing the final neural network against the best regression model from Part 1

Repository Structure

intro_to_ai_final_project/
├── notebooks/
│   ├── part1-regression.ipynb
│   ├── part2-classification.ipynb
│   ├── part3-pytorch-nn.ipynb
│   └── bonus-regression-pytorch.ipynb
├── INTRO_TO_AI.pdf
└── Intro_to_AI_Project_Instructions.pdf