Lectures related to Line Search: Optimization Basics

Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.

Optimization Techniques: Convexity in Machine Learning

Covers optimization techniques in machine learning, focusing on convexity and its implications for efficient problem-solving.

Optimization without Constraints: Gradient Method

Covers optimization without constraints using the gradient method to find the function's minimum.

Optimization Techniques: Stochastic Gradient Descent and Beyond

Discusses optimization techniques in machine learning, focusing on stochastic gradient descent and its applications in constrained and non-convex problems.

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.

Optimization with Constraints: KKT Conditions

Covers the KKT conditions for optimization with constraints, essential for solving constrained optimization problems efficiently.

Optimization Basics: Unconstrained Optimization and Gradient Descent

Covers optimization basics, including unconstrained optimization and gradient descent methods for finding optimal solutions.

Optimization Methods in Machine Learning

Explores optimization methods in machine learning, emphasizing gradients, costs, and computational efforts for efficient model training.

Introduction to Optimization and Operations Research

Covers fundamental concepts of optimization and operations research, exploring real-world examples and key topics over a semester.

Semi-Definite Programming

Covers semi-definite programming and optimization over positive semidefinite cones.

Optimization: Gradient Descent and Subgradients

Explores optimization methods like gradient descent and subgradients for training machine learning models, including advanced techniques like Adam optimization.

Gradient Descent: Principles and Applications

Covers gradient descent, its principles, applications, and convergence rates in optimization for machine learning.

Stochastic Gradient Descent: Non-convex Optimization Techniques

Discusses Stochastic Gradient Descent and its application in non-convex optimization, focusing on convergence rates and challenges in machine learning.

Recommender Systems: Matrix Factorization

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

Mathematics of Data: Computation Role

Explores the role of computation in data mathematics, focusing on iterative methods, optimization, estimators, and descent principles.

Stochastic Optimization: Algorithms and Methods

Explores stochastic optimization algorithms and methods for convex problems with smooth and nonsmooth risks.

Proximal Gradient Descent: Optimization Techniques in Machine Learning

Discusses proximal gradient descent and its applications in optimizing machine learning algorithms.

Optimality of Convergence Rates: Accelerated Gradient Descent

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

Word Embeddings: Modeling Word Context and Similarity

Covers word embeddings, modeling word context and similarity in a low-dimensional space.

Introduction to Optimization

Covers the basics of optimization, including historical perspectives, mathematical formulations, and practical applications in decision-making problems.