uzairakbar/info-retrieval

Comprehensive collection of Jupyter notebooks and theory documents covering dimensionality reduction (PCA, Kernel PCA, LDA), statistical learning (k-NN, logistic regression, SVM), and neural network fundamentals using NumPy and scikit-learn. Includes practical labs on word embeddings, convex optimization, and kernel methods alongside theoretical assignments on the curse of dimensionality and the kernel trick. Paired with multiple benchmark datasets (MNIST, Yale Faces, IMDB sentiment, MIT texture) for hands-on implementation of high-dimensional data retrieval and classification techniques.

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