Lectures related to Stochastic Gradient Descent: Optimization Techniques

Optimality of Convergence Rates: Accelerated Gradient Descent

Explores the optimality of convergence rates in convex optimization, focusing on accelerated gradient descent and adaptive methods.

Concise Signal Models and Compressive Sensing

Explores concise signal models, compressive sensing, sparsity, atomic norms, and non-smooth minimization using subgradient descent.

Gradient Descent: Linear Regression

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

Optimization in Machine Learning: Gradient Descent

Covers optimization in machine learning, focusing on gradient descent for linear and logistic regression, stochastic gradient descent, and practical considerations.

Recommender Systems: Matrix Factorization

Explores matrix factorization in recommender systems, covering optimization, evaluation metrics, and challenges in scaling.

Stochastic Gradient Descent

Explores stochastic gradient descent optimization and the Mean-Field Method in neural networks.