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Machine Learning

A collection of machine learning algorithms implemented in MATLAB/Octave and Python, covering supervised learning, unsupervised learning, and recommender systems.

Projects

# Topic Description Language
01 Linear Regression Univariate and multivariate regression with gradient descent and normal equations MATLAB
02 Logistic Regression Binary classification with regularization and polynomial decision boundaries MATLAB
03 Multiclass Classification One-vs-all logistic regression and pre-trained neural network for digit recognition MATLAB
04 Neural Networks Two-layer neural network trained from scratch with backpropagation MATLAB
05 Bias-Variance Tradeoff Polynomial regression with learning curves and regularization parameter selection MATLAB
06 Support Vector Machines Linear and RBF kernel SVMs, plus a spam email classifier MATLAB
07 K-Means Clustering Clustering algorithm applied to 2D data and image compression MATLAB
08 Principal Component Analysis Dimensionality reduction on 2D data, face images, and pixel data MATLAB
09 Anomaly Detection Gaussian-based anomaly detection for server monitoring MATLAB
10 Recommender Systems Collaborative filtering for movie recommendations (MovieLens dataset) MATLAB
11 Normal Distribution Gaussian PDF utilities, visualization, and maximum likelihood estimation MATLAB
12 Linear Regression (Python) Linear regression from scratch in Python with gradient descent Python

Highlights

Supervised Learning

  • Regression: Linear regression (univariate/multivariate), polynomial regression, regularization
  • Classification: Logistic regression, one-vs-all, neural networks, SVMs, spam classification

Unsupervised Learning

  • Clustering: K-Means with image compression application
  • Dimensionality Reduction: PCA with eigenfaces and data visualization
  • Anomaly Detection: Gaussian-based outlier identification

Recommender Systems

  • Collaborative filtering for movie recommendations

Sample Visualizations

Linear Regression Logistic Regression K-Means Clustering
SVM Classification PCA Anomaly Detection

Credits

The MATLAB exercises are based on Andrew Ng's Machine Learning course on Coursera (Stanford University / DeepLearning.ai). All exercises were completed by Keivan Hassani Monfared.

License

This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details.