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This assignment focuses on developing a handwritten digit recognition system using techniques such as PCA, Isomap, and t-SNE for dimensionality reduction and visualization. The project entails managing a dataset of 5,000 samples of handwritten digits and implementing classifiers to evaluate performance based on classification error. Students will apply machine learning concepts to preprocess data, execute classification experiments, and analyze results.

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