Skip to main content
Graph
Search
fr
en
Login
Search
All
Categories
Concepts
Courses
Lectures
MOOCs
People
Practice
Publications
Startups
Units
Show all results for
Home
Lecture
Optimal Distributed Control: Projected GD for Locally Optimal Controllers
Graph Chatbot
Related lectures (32)
Linear Response and Complex Diffusivity
Explores martingale-based linear response, complex diffusivity, and Nyquist relation in stochastic systems with time-dependent perturbation.
Stochastic Integration
Covers stochastic integration and exchange mobility for mathematics students.
Stochastic Calculus: Integrals and Processes
Explores stochastic calculus, emphasizing integrals, processes, martingales, and Brownian motion.
Stochastic Differential Equations
Covers Stochastic Differential Equations, Wiener increment, Ito's lemma, and white noise integration in financial modeling.
Quadratic Variation: Martingales and Stochastic Integrals
Explores quadratic variation in martingales and stochastic integrals, emphasizing their properties and extensions.
Signals and Systems: Introduction
Introduces the fundamental concepts of Signals and Systems course, emphasizing practical applications of system behavior analysis.
Mean Field Theory: Stochastic Analysis and Applications
Explores classical mean field theory, local interactions, and examples like individual-based SIR models and raindrop formation.
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.
Multivariable Control: Gaussian Processes and Linear Systems
Explores Gaussian processes, linear systems, transformations, and noise properties in multivariable control applications.
Modeling Introduction
Explores the importance of modeling to understand and control complex systems efficiently.
Flow Equations: Singular SPDEs and Quantum Field Theory
Covers recent research on singular SPDEs and their applications in quantum field theory.
Neural Network Training
Covers the training process of a neural network, including feedforward, cost function, gradient checking, and visualization of hidden layers.
Previous
Page 2 of 2
Next