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Explores linear regression fundamentals, non-linear regression issues, and R-squared goodness of fit, with examples like Anscombe's quartet and the Datasaurus dataset.
Explores learning the kernel function in convex optimization, focusing on predicting outputs using a linear classifier and selecting optimal kernel functions through cross-validation.
Explores adversarial thinking, common weaknesses, and ineffective defenses in software systems, emphasizing the importance of mitigating prevalent vulnerabilities.
