Machine Learning

Collection of coding projects from my CSE 6363 class

This page showcases a collection of coding projects, experiments, and assignments from my CSE 6363 (Machine Learning) class under Dr. Alexander Dillhoff. The projects focus on applying ML concepts to real-world datasets and problems using Python, PyTorch, and scikit-learn.

Project Page

You can find the GitHub repository here here.

📂 Projects Included

  • ** Regression from Scratch**
    Implemented linear and logistic regression pipelines with gradient descent optimization and visualized decision boundaries.

  • Deep Learning for Image Classification
    Trained and fine-tuned convolutional neural networks (CNNs), applied regularization, and implemented transfer learning

  • Clustering Algorithms
    Compared K-Means, DBSCAN, and hierarchical clustering for high-dimensional data visualization.

  • Support Vector Machines (SVMs)
    Applied SVMs with different kernels to classify non-linear datasets.

  • Decision Trees and Random Forests
    Built tree-based models for classification tasks and analyzed feature importance.

  • Reinforcement Learning
    Q-Learning and Policy Iteration on Frozen Lake environment. Deep Q-Learning on Atari Game Envrioment