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Related lectures (6)
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Convex Optimization: Theory and Applications
Explores the theory and applications of convex optimization, covering topics such as log-determinant function, affine transformations, and relative entropy.
Gauss-Jordan Elimination Method
Explains the Gauss-Jordan elimination method for solving linear equations and discusses main and free variables.
Kernel Methods: Feature Mapping and Kernel Matrix
Explores feature mapping in Kernel Methods and the properties of the Kernel matrix.
Characteristic Functions: Properties and Applications
Covers characteristic functions, their properties, inversion formula, and factorization property in probability theory.
Convex Functions: Theory and Applications
Explores convex functions, including checking convexity, transformations, examples, minimization, geometric intuition, Schur's Lemma, distance function, perspective function, and relative entropy.
Kernel Trick: Understanding Machine Learning
Explores the kernel trick in machine learning, enabling high-dimensional operations without explicit coordinate calculations.
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