Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Home

Lecture

Stochastic Optimization: Algorithms and Methods

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (43)

Optimization Techniques: Convexity and Algorithms in Machine Learning Log in to Mediaspace to watch this video

Covers optimization techniques in machine learning, focusing on convexity, algorithms, and their applications in ensuring efficient convergence to global minima.

Optimization Techniques: Stochastic Gradient Descent and Beyond Log in to Mediaspace to watch this video

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

Convex Optimization: Gradient Algorithms Log in to Mediaspace to watch this video

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

Proximal Gradient Descent: Optimization Techniques in Machine Learning Log in to Mediaspace to watch this video

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

Faster Gradient Descent: Projected Optimization Techniques Log in to Mediaspace to watch this video

Covers faster gradient descent methods and projected gradient descent for constrained optimization in machine learning.

Linear Models for Classification: Logistic Regression and SVM Log in to Mediaspace to watch this video

Covers linear models for classification, focusing on logistic regression and support vector machines.

Optimization Techniques: Gradient Descent and Convex Functions Log in to Mediaspace to watch this video

Provides an overview of optimization techniques, focusing on gradient descent and properties of convex functions in machine learning.

Coordinate Descent: Optimization Strategies Log in to Mediaspace to watch this video

Explores coordinate descent optimization strategies, emphasizing simplicity in optimization through one-coordinate updates and discussing the implications of different approaches.

Convex Optimization: Theory and Applications Log in to Mediaspace to watch this video

Explores convex optimization theory, covering local and global minima, convex functions, and applications in various fields.

Convex Optimization: Duality and KKT Conditions Log in to Mediaspace to watch this video

Explores convex optimization duality, KKT conditions, and financial arbitrage detection.

MCMC with Metropolis Log in to Mediaspace to watch this video

Covers the implementation of Markov Chain Monte Carlo (MCMC) with the Metropolis algorithm for sampling from posterior distributions.

Convex Optimization: Generalized Inequalities Log in to Mediaspace to watch this video

Explores problems with generalized inequalities in convex optimization and the equivalence between SOCP and SDP.

Optimal Transport: Theory and Applications Log in to Mediaspace to watch this video

Covers the theory and applications of optimal transport, focusing on infimal convolution and Kantorovich potentials.

Lyapunov Stability: Convex Optimization Log in to Mediaspace to watch this video

Covers Lyapunov stability in convex optimization and its implementation through trajectory plots.

Solving Linear Programs: SIMPLEX Method Log in to Mediaspace to watch this video

Explains the SIMPLEX method for solving linear programs and optimizing the solution through basis variable manipulation.

Powder Characterisation: Physical, Chemical, Morphology Log in to Mediaspace to watch this video

Explores the physical, chemical, and morphological analysis of powders, emphasizing sampling accuracy and quality control.

Introduction to Shortest Path Log in to Mediaspace to watch this video

Introduces the concept of shortest path, discussing weighted paths, Hamiltonian paths, and path optimization algorithms.

Basics of Linear Regression Log in to Mediaspace to watch this video

Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.

Atomistic Computer Modelling of Materials: Simulating and Sampling Log in to Mediaspace to watch this video

Covers simulating and sampling in atomistic computer modelling of materials.

Feature Expansion: Kernels and KNN Log in to Mediaspace to watch this video

Covers feature expansion, kernels, and K-nearest neighbors, including non-linearity, SVM, and Gaussian kernels.

Page 2 of 3