Minimax Approximation and the Exchange Algorithm
In this video we'll discuss minimax approximation. This is a method of approximating functions by minimisation of the infinity (uniform) norm. The exchange algorithm is an iterative method of finding the approximation which minimises the infinity norm. FAQ : How do you make these animatio
From playlist Approximation Theory
Approximating Functions in a Metric Space
Approximations are common in many areas of mathematics from Taylor series to machine learning. In this video, we will define what is meant by a best approximation and prove that a best approximation exists in a metric space. Chapters 0:00 - Examples of Approximation 0:46 - Best Aproximati
From playlist Approximation Theory
How to find the position function given the acceleration function
š Learn how to approximate the integral of a function using the Reimann sum approximation. Reimann sum is an approximation of the area under a curve or between two curves by dividing it into multiple simple shapes like rectangles and trapezoids. In using the Reimann sum to approximate the
From playlist Riemann Sum Approximation
Numerical Optimization Algorithms: Step Size Via Line Minimization
In this video we discuss how to choose the step size in a numerical optimization algorithm using the Line Minimization technique. Topics and timestamps: 0:00 ā Introduction 2:30 ā Single iteration of line minimization 22:58 ā Numerical results with line minimization 30:13 ā Challenges wit
From playlist Optimization
Polynomial approximations -- Calculus II
This lecture is on Calculus II. It follows Part II of the book Calculus Illustrated by Peter Saveliev. The text of the book can be found at http://calculus123.com.
From playlist Calculus II
Linear Approximation to f(x,y) = x^2y^2 + x
Multivariable Calculus: Find the linear approximation to the function f(x, y) = x^2 y^2 + x at the point (2, 3). Then approximate (2.1)^2 (2.9)^2 + 2.1. For more videos like this one, please visit the Multivariable Calculus playlist at my channel.
From playlist Calculus Pt 7: Multivariable Calculus
Business Math - The Simplex Method (8 of 15) Standard Minimization - The Dual Problem
Visit http://ilectureonline.com for more math and science lectures! In this video I will find the dual problem of a standard minimization problem. Next video in this series can be seen at: http://youtu.be/xJ78dUmsl34
From playlist BUSINESS MATH - THE SIMPLEX METHOD
Reinforcement Learning 10: Classic Games Case Study
David Silver, Research Scientist, discusses classic games as part of the Advanced Deep Learning & Reinforcement Learning Lectures.
From playlist DeepMind x UCL | Reinforcement Learning Course 2018
Chao Gao: Statistical Optimality and Algorithms for Top-K Ranking - Lecture 2
CIRM VIRTUAL CONFERENCE In the first presentation, we will consider the top-K ranking problem. The statistical properties of two popular algorithms, MLE and rank centrality (spectral ranking) will be precisely characterized. In terms of both partial and exact recovery, the MLE achieves op
From playlist Virtual Conference
Fastest Identification in Linear Systems by Alexandre Proutiere
Program Advances in Applied Probability II (ONLINE) ORGANIZERS: Vivek S Borkar (IIT Bombay, India), Sandeep Juneja (TIFR Mumbai, India), Kavita Ramanan (Brown University, Rhode Island), Devavrat Shah (MIT, US) and Piyush Srivastava (TIFR Mumbai, India) DATE: 04 January 2021 to 08 Januar
From playlist Advances in Applied Probability II (Online)
Learning Minimax Estimators Via Online Learning by Praneeth Netrapalli
PROGRAM: ADVANCES IN APPLIED PROBABILITY ORGANIZERS: Vivek Borkar, Sandeep Juneja, Kavita Ramanan, Devavrat Shah, and Piyush Srivastava DATE & TIME: 05 August 2019 to 17 August 2019 VENUE: Ramanujan Lecture Hall, ICTS Bangalore Applied probability has seen a revolutionary growth in resear
From playlist Advances in Applied Probability 2019
Mengdi Wang: "On the statistical complexity of reinforcement learning"
Intersections between Control, Learning and Optimization 2020 "On the statistical complexity of reinforcement learning" Mengdi Wang - Princeton University Abstract: Recent years have witnessed increasing empirical successes in reinforcement learning (RL). However, many theoretical questi
From playlist Intersections between Control, Learning and Optimization 2020
Nevanlinna Prize Lecture: Equilibria and fixed points ā Constantinos Daskalakis ā ICM2018
Equilibria, fixed points, and computational complexity Constantinos Daskalakis Abstract: The concept of equilibrium, in its various forms, has played a central role in the development of Game Theory and Economics. The mathematical properties and computational complexity of equilibria are
From playlist Special / Prizes Lectures
Solving a Standard Minimization Problem Using The Simplex Method (Duality)
This video explains how to solve a standard minimization problem using the simplex method Site: http://mathispower4u.com
From playlist The Simplex Method
Stochastic Gradient Descent and Machine Learning (Lecture 4) by Praneeth Netrapalli
PROGRAM: BANGALORE SCHOOL ON STATISTICAL PHYSICS - XIII (HYBRID) ORGANIZERS: Abhishek Dhar (ICTS-TIFR, India) and Sanjib Sabhapandit (RRI, India) DATE & TIME: 11 July 2022 to 22 July 2022 VENUE: Madhava Lecture Hall and Online This school is the thirteenth in the series. The schoo
From playlist Bangalore School on Statistical Physics - XIII - 2022 (Live Streamed)
Nonconvex Minimax Optimization - Chi Ji
Seminar on Theoretical Machine Learning Topic: Nonconvex Minimax Optimization Speaker: Chi Ji Affiliation: Princeton University; Member, School of Mathematics Date: November 20, 2019 For more video please visit http://video.ias.edu
From playlist Mathematics
6. Search: Games, Minimax, and Alpha-Beta
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston In this lecture, we consider strategies for adversarial games such as chess. We discuss the minimax algorithm, and how alpha-beta pruning improves its efficiency
From playlist MIT 6.034 Artificial Intelligence, Fall 2010
Minimization problems and the closest point to a subspace. Approximating sin x with polynomials, better on average than with the Taylor polynomial.
From playlist Linear Algebra Done Right