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Lecture
Convex Optimization: Examples of Convex Functions
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Gradient Descent: Linear Regression
Covers the concept of gradient descent for linear regression, explaining the iterative process of updating parameters.
Optimization Techniques: Convexity in Machine Learning
Covers optimization techniques in machine learning, focusing on convexity and its implications for efficient problem-solving.
Convex Optimization: Gradient Descent
Explores VC dimension, gradient descent, convex sets, and Lipschitz functions in convex optimization.
Convex Optimization: Epigraphs
Explores epigraphs, convexity of bivariate functions, and log-sum-exp functions in convex optimization.
Convex Optimization: Sets and Functions
Introduces convex optimization through sets and functions, covering intersections, examples, operations, gradient, Hessian, and real-world applications.
Logistic Regression: Cost Functions & Optimization
Explores logistic regression, cost functions, gradient descent, and probability modeling using the logistic sigmoid function.
Logistic Regression: Interpretation & Feature Engineering
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Nonparametric Regression: Kernel-Based Estimation
Covers nonparametric regression using kernel-based estimation techniques to model complex relationships between variables.
Optimization Techniques: Convexity and Algorithms in Machine Learning
Covers optimization techniques in machine learning, focusing on convexity, algorithms, and their applications in ensuring efficient convergence to global minima.
Convex Optimization: Convex Functions
Covers the concept of convex functions and their applications in optimization problems.