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Explores machine learning security, including model stealing, altering outputs, adversarial conditions, and privacy challenges, emphasizing the importance of addressing biases in machine learning models.
Delves into deep learning's dimensionality, data representation, and performance in classifying large-dimensional data, exploring the curse of dimensionality and the neural tangent kernel.
Introduces Support Vector Clustering (SVC) using a Gaussian kernel for high-dimensional feature space mapping and explains its constraints and Lagrangian.
Delves into advanced data preprocessing techniques, covering categorical encoding, missing data handling, and unbalanced datasets, emphasizing performance metrics and classifier comparison.
Explores data collection, feature selection, model building, and performance evaluation in machine learning, emphasizing feature engineering and model selection.
