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University of Pennsylvania Machine Learning Course Projects

This repository contains the projects and implementations completed as part of the University of Pennsylvania Machine Learning course. Throughout the course, various machine learning algorithms were implemented, providing hands-on experience in model evaluation, hyperparameter tuning, and data preprocessing. The focus was on both understanding the theoretical concepts and applying them to real-world datasets.

Algorithms Implemented

  1. Linear Regression

    • Implemented using both analytical and gradient descent methods.
    • Explored feature scaling and regularization techniques (Lasso and Ridge).

  2. Linear Discriminant Analysis (LDA)

    • Analyzed class separability and dimensionality reduction.
    • Applied LDA to classification problems.

  3. Logistic Regression

    • Implemented for binary classification with cross-entropy loss.
    • Extended to multi-class classification with softmax.

  4. Decision Trees

    • Implemented decision trees using recursive splitting and entropy/Gini index as criteria.
    • Pruned trees to avoid overfitting.

  5. Support Vector Machines (SVM)

    • Implemented SVM for classification tasks with kernel methods.
    • Analyzed hyperparameter effects such as regularization (C) and kernel selection.

  6. Naive Bayes

    • Implemented Gaussian and Multinomial Naive Bayes for classification problems.
    • Tested model accuracy on text classification tasks.

  7. K-Nearest Neighbors (KNN)

    • Implemented KNN.
    • Analyzed the effect of varying 'k' on model performance.

  8. K-Means Clustering

    • Implemented the K-means algorithm for unsupervised clustering.
    • Evaluated cluster quality using silhouette scores.

  9. Principal Component Analysis (PCA)

    • Implemented PCA for dimensionality reduction.
    • Visualized high-dimensional data in lower-dimensional spaces.

  10. Multilayer Perceptrons (MLP)

    • Implemented neural networks using backpropagation.
    • Tuned network architectures and hyperparameters (learning rate, number of layers).

Key Concepts

  • Model Evaluation: Implemented techniques such as cross-validation, confusion matrix analysis, and precision/recall.
  • Hyperparameter Tuning: Explored grid search and random search for optimal hyperparameters.
  • Data Preprocessing: Addressed missing data, feature scaling, and normalization.