AI Coursework Portfolio

This repository contains implementations of fundamental Artificial Intelligence and Deep Learning algorithms, developed during my coursework at the University of Wisconsin-Madison (CS 540: Introduction to AI).

The projects demonstrate my proficiency in PyTorch, Reinforcement Learning, and Adversarial Search, focusing on building models and agents from scratch to understand their underlying mechanics.

📂 Project Highlights

1. LeNet-5 CNN Implementation (PyTorch)

• Path: computer_vision/lenet_cnn_classifier.py
• Description: A manual implementation of the LeNet-5 Convolutional Neural Network architecture for image classification.
• Key Technical Features:
  • Architecture Design: Constructed the network using torch.nn.Module, defining Convolutional layers, Max Pooling, and Fully Connected layers.
  • Training Loop: Implemented the full optimization pipeline from scratch, including:
    • Forward propagation
    • Loss computation (CrossEntropyLoss)
    • Manual gradient handling (optimizer.zero_grad(), loss.backward(), optimizer.step()) to demonstrate understanding of Autograd.
  • Dimensionality Management: Explicit handling of tensor shape transformations between convolutional and linear layers.

2. Tabular Q-Learning Agent

• Path: reinforcement_learning/rl_frozenlake_agent.py
• Description: An off-policy TD control agent trained to solve the gymnasium FrozenLake-v1 environment.
• Key Technical Features:
  • Q-Table Update: Implemented the core Bellman update equation: $Q(s,a) \leftarrow (1-\alpha)Q(s,a) + \alpha(r + \gamma \max_{a'} Q(s',a'))$.
  • Exploration vs. Exploitation: Designed an $\epsilon$-greedy strategy with epsilon decay to balance exploring the environment and exploiting learned policies.
  • State-Action Mapping: Managed state-action pairs using Python dictionaries for efficient lookup.

3. Minimax Game Agent (Teeko)

• Path: search_algorithms/minimax_game_agent.py
• Description: An adversarial AI agent capable of playing the strategy board game Teeko.
• Key Technical Features:
  • Minimax Algorithm: Implemented a recursive depth-limited search to evaluate game states.
  • Heuristic Evaluation: Designed a custom evaluation function that scores board states based on piece position, control of the center, and proximity to winning configurations (4-in-a-row).
  • Successor Generation: Logic to handle different game phases (Drop Phase vs. Movement Phase).

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🛠 Skills & Tools

• Languages: Python 3
• Deep Learning: PyTorch, Torchvision
• Reinforcement Learning: Gymnasium
• Scientific Computing: NumPy
• Version Control: Git

🚀 Usage

To run the Convolutional Neural Network training:

cd computer_vision
pip install -r requirements.txt
python lenet_cnn_classifier.py

To train the Q-Learning agent:

cd reinforcement_learning
pip install -r requirements.txt
python rl_frozenlake_agent.py