Convex Optimization: Nonlinear Dimensionality Reduction

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Explores linear models, regression, multi-output prediction, classification, non-linearity, and gradient-based optimization.

Covers the recap of Support Vector Regression with a focus on convex optimization and its equivalence to Gaussian Process Regression.

Covers the concept of kernel regression and K-nearest neighbors for making data linearly separable.

Introduces linear models for supervised learning, covering overfitting, regularization, and kernels, with applications in machine learning tasks.

Covers the transition from linear to nonlinear models, focusing on k-NN and feature expansion techniques.

Explores gradient descent, loss functions, and optimization techniques in neural network training.

Covers the fundamentals of advanced machine learning, emphasizing practical applications through interactive exercises and projects.

Covers the concept of gradient descent for linear regression, explaining the iterative process of updating parameters.

Covers convex optimization problems and gradient-based algorithms to find the global minimum.

Explores convexifying nonconvex problems through SVM and dimensionality reduction techniques.