Lecture 3 | Convex Optimization I (Stanford)
Professor Stephen Boyd, of the Stanford University Electrical Engineering department, lectures on convex and concave functions for the course, Convex ...
Stanford
Lecture 2 | Convex Optimization I (Stanford)
Guest Lecturer Jacob Mattingley covers convex sets and their applications in electrical engineering and beyond for the course, Convex Optimization I (EE 364A).
Stanford
Lecture 8 | Convex Optimization I (Stanford)
Professor Stephen Boyd, of the Stanford University Electrical Engineering department, lectures on duality in the realm of electrical engineering and how it is ...
Stanford
Mod-01 Lec-52 Norms for Vectors, Matrices, Signals and Linear Systems
Optimal Control by Prof. G.D. Ray,Department of Electrical Engineering,IIT Kharagpur.For more details on NPTEL visit http://nptel.ac.in.
nptelhrd
2. Bayesian Optimization
UAI 2018
Daniel Kuhn: Data-driven and Distributionally Robust Optimization and Applications -- Part 1/2
Speaker: Daniel Kuhn (EPFL) Event: DTU CEE Summer School 2018 on "Modern Optimization in Energy Systems", 25-29 June 2018, Copenhagen, Denmark ...
DTU CEE Lectures: Optimization in Energy Systems
AI Disruption of Quantitative Finance: From Forecasting, to Generative Models to Optimization
Democratisation of AI and invent of big data technologies has disrupted the quantitative finance practice. Various ML and DL models provide the next generation ...
Databricks
| Optimization Problems under Uncertain Environment| Dr.K.Ganesan |?
Fuzzy mathematics forms a branch of mathematics related to fuzzy set theory and fuzzy logic. Since its inception a few decades ago, the theory of fuzzy sets has ...
SJ Math
Better Algorithms for Bin Packing
Better Algorithms for Bin Packing UW Assistant Mathematics Professor, Thomas Rothvoss lectures about bin packing, one of the fundamental NP-hard problems ...
UW Video
Lecture 5 | Convex Optimization I (Stanford)
Professor Stephen Boyd, of the Stanford University Electrical Engineering department, lectures on the different problems that are included within convex ...
Stanford
Techniques for combinatorial optimization: Spectral Graph Theory and Semidefinite Programming
The talk focuses on expander graphs in conjunction with the combined use of SDPs and eigenvalue techniques for approximating optimal solutions to ...
Microsoft Research
State Space, Part 4: What is LQR control?
Check out the other videos in the series: Part 1 - The state space equations: https://youtu.be/hpeKrMG-WP0 Part 2 - Pole placement: ...
MATLAB
State Space, Part 1: Introduction to State-Space Equations
Let's introduce the state-space equations, the model representation of choice for modern control. This video is the first in a series on MIMO control and will ...
MATLAB
Solving Simple Stochastic Optimization Problems with Gurobi
The importance of incorporating uncertainty into optimization problems has always been known; however, both the theory and software were not up to the ...
Gurobi Optimization
Introduction to Linear Quadratic Regulator (LQR) Control
In this video we introduce the linear quadratic regulator (LQR) controller. We show that an LQR controller is a full state feedback controller where the gain matrix ...
Christopher Lum
Mod-01 Lec-30 Dynamic Optimization Problem : Basic Concepts & Necessary and Sufficient Conditions
Optimal Control by Prof. G.D. Ray,Department of Electrical Engineering,IIT Kharagpur.For more details on NPTEL visit http://nptel.ac.in.
nptelhrd
Lecture 10 | Convex Optimization I (Stanford)
Professor Stephen Boyd, of the Stanford University Electrical Engineering department, lectures on approximation and fitting within convex optimization for the ...
Stanford
TutORial: Bayesian Optimization
By Peter Frazier. Bayesian optimization is widely used for tuning deep neural networks and optimizing other black-box objective functions that take a long time to ...
INFORMS
Lecture 17 | Convex Optimization I (Stanford)
Professor Stephen Boyd, of the Stanford University Electrical Engineering department, continues his lecture on equality constrained minimization for the course, ...
Stanford
Mod-10 Lec-23 Static Optimization: An Overview
Advanced Control System Design by Radhakant Padhi, Department of Aerospace Engineering, IISC Bangalore For more details on NPTEL visit ...
nptelhrd
Quantum Computing 2019: A Machine of a Different Kind | D-Wave Webinar
In 1982, Richard Feynman envisioned a "machine of a different kind," one that could simulate physics by doing exactly what nature does. In this webinar ...
D-Wave Systems
Lieven Vandenberghe: "Bregman proximal methods for semidefinite optimization."
Intersections between Control, Learning and Optimization 2020 "Bregman proximal methods for semidefinite optimization." Lieven Vandenberghe - University of ...
Institute for Pure & Applied Mathematics (IPAM)
Introduction to Optimization and Optimal Control using the software packages CasADi and ACADO
Adriaen Verheyleweghen and Christoph Backi Virtual Simulation Lab seminar series http://www.virtualsimlab.com.
Virtual Simulation Lab
Design and Optimization of Dielectric Metasurfaces
Research in the field of dielectric metasurfaces has recently enabled wavelength-scale thickness flat optical elements that promise to reduce the form factor of ...
Microsoft Research
Lecture 11 | Convex Optimization I (Stanford)
Professor Stephen Boyd, of the Stanford University Electrical Engineering department, lectures on how statistical estimation can be used in convex optimization ...
Stanford
Lecture 6 | Machine Learning (Stanford)
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses the applications of naive ...
Stanford
Real-time optimization algorithms for dynamic walking, running, and manipulating robots
The worlds largest technology companies and science funding agencies are investing millions in robotics. They anticipate robots that perform work as first ...
UW Video
Solve Differential Equations in MATLAB and Simulink
This introduction to MATLAB and Simulink ODE solvers demonstrates how to set up and solve either one or multiple differential equations. The equations can be ...
APMonitor.com
11. Unconstrained Optimization; Newton-Raphson and Trust Region Methods
MIT 10.34 Numerical Methods Applied to Chemical Engineering, Fall 2015 View the complete course: http://ocw.mit.edu/10-34F15 Instructor: James Swan ...
MIT OpenCourseWare
Hierarchical reinforcement learning - Doina Precup
Doina Precup research interests are in the areas of reinforcement learning, deep learning, time series analysis, and diverse applications. In this talk, Dr. Precup ...
Stanford
Reinforcement Learning 6: Policy Gradients and Actor Critics
Hado Van Hasselt, Research Scientist, discusses policy gradients and actor critics as part of the Advanced Deep Learning & Reinforcement Learning Lectures.
DeepMind
6. L1 & L2 Regularization
We introduce "regularization", our main defense against overfitting. We discuss the equivalence of the penalization and constraint forms of regularization (see ...
Inside Bloomberg
The Hessian matrix
The Hessian matrix is a way of organizing all the second partial derivative information of a multivariable function.
Khan Academy
An Introduction to Graph Neural Networks: Models and Applications
MSR Cambridge, AI Residency Advanced Lecture Series An Introduction to Graph Neural Networks: Models and Applications Got it now: "Graph Neural ...
Microsoft Research
Fuzzy optimization (1)
Water Resources Systems : Modeling Techniques and Analysis by Prof. P.P. Mujumdar, Department of Civil Engineering, IISc Bangalore. For more details on ...
nptelhrd
Mod-02 Lec-04 An Overview of Static Optimization -- I
Optimal Control, Guidance and Estimation by Dr. Radhakant Padhi, Department of Aerospace Engineering, IISc Bangalore. For more details on NPTEL visit ...
nptelhrd
The Importance of Better Models in Stochastic Optimization...
John Duchi (Stanford University) https://simons.berkeley.edu/talks/tbd-28 Robust and High-Dimensional Statistics.
Simons Institute
Phebe Vayanos, Robust Optimization & Sequential Decision-Making
CompSustNet
Mod-01 Lec-05 Unconstrained optimization problem (Numerical Techniques)
Optimal Control by Prof. G.D. Ray,Department of Electrical Engineering,IIT Kharagpur.For more details on NPTEL visit http://nptel.ac.in.
nptelhrd
Optimization I
Ben Recht, UC Berkeley Big Data Boot Camp http://simons.berkeley.edu/talks/ben-recht-2013-09-04.
Simons Institute
CS231n Winter 2016: Lecture 3: Linear Classification 2, Optimization
Stanford Winter Quarter 2016 class: CS231n: Convolutional Neural Networks for Visual Recognition. Lecture 3. Get in touch on Twitter @cs231n, or on Reddit ...
Andrej Karpathy
On Gradient-Based Optimization: Accelerated, Stochastic and Nonconvex
Many new theoretical challenges have arisen in the area of gradient-based optimization for large-scale statistical data analysis, driven by the needs of ...
Microsoft Research
