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Covers overfitting, regularization, and cross-validation in machine learning, exploring polynomial curve fitting, feature expansion, kernel functions, and model selection.
Explores learning the kernel function in convex optimization, focusing on predicting outputs using a linear classifier and selecting optimal kernel functions through cross-validation.
Provides an overview of MicroC/OS-II, a real-time kernel with multitasking capabilities and deterministic functions, covering topics such as task management, kernel, and intertask communication.
