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
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
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
Approximation & Estimation | Numbers | Maths | FuseSchool
An approximation is anything that is similar, but not exactly the same as something else. For example, if you were to say a 57 minute journey would take “about an hour”, you would be approximating. A value can be approximated by rounding, usually to a value that it is easier to work with
From playlist MATHS: Numbers
Polynomial approximation of functions (part 1)
Using a polynomial to approximate a function at f(0). More free lessons at: http://www.khanacademy.org/video?v=sy132cgqaiU
From playlist Calculus
Error bounds for Taylor 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
How to find the position function given the acceleration function
👉 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
Polynomial approximation of functions (part 2)
Approximating a function with a polynomial by making the derivatives equal at f(0) (Maclauren Series) More free lessons at: http://www.khanacademy.org/video?v=3JG3qn7-Sac
From playlist Calculus
We use the differential properties of the exponential and logistic curves to fit an equation to real world data. This is part of the professional development course https://www.openlearning.com/courses/populationgrowthandthelogisticcurve offered by the University of New South Wales.
From playlist Mathematics in The Modern World: PD courses for teachers
Lecture 08 - Bias-Variance Tradeoff
Bias-Variance Tradeoff - Breaking down the learning performance into competing quantities. The learning curves. Lecture 8 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunes.apple.com/us/course/ma
From playlist Machine Learning Course - CS 156
Functions are Vectors? Fourier Series and an Illustration of the Why #SoME2
This is my submission to 3blue1brown's Summer of Math Exposition 2. Link to Khan Academy Video on Dot Product Linearity and Symmetry: https://www.khanacademy.org/math/linear-algebra/vectors-and-spaces/dot-cross-products/v/proving-vector-dot-product-properties Link to 3blue1brown Video on
From playlist Summer of Math Exposition 2 videos
Mathematics for Machine Learning - Multivariate Calculus - Full Online Specialism
Welcome to the “Mathematics for Machine Learning: Multivariate Calculus” course, offered by Imperial College London. This video is an online specialisation in Mathematics for Machine Learning (m4ml) hosted by Coursera. For more information on the course and to access the full experience
From playlist Mathematics for Machine Learning - Multivariate Calculus
8ECM Invited Lecture: Nick Trefethen
From playlist 8ECM Invited Lectures
Introduction to Slope Fields (Differential Equations 9)
https://www.patreon.com/ProfessorLeonard A constructive approach to Slope Fields and how they work. Individual exploration with a Computer Graphing application is highly recommended.
From playlist Differential Equations
Lecture 18 - Continous State MDP & Model Simulation | Stanford CS229: Machine Learning (Autumn 2018)
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3njDdzN Andrew Ng Adjunct Professor of Computer Science https://www.andrewng.org/ To follow along with the course schedule and syllabus, visit: http://cs229.sta
From playlist Stanford CS229: Machine Learning Full Course taught by Andrew Ng | Autumn 2018
Adrian Baddeley: The Poisson-saddlepoint approximation
Gibbs spatial point processes are important models in theoretical physics and in spatial statistics. After a brief survey of Gibbs point processes, we will present a method for approximating their most important characteristic, the intensity of the process. The method has some affinity wit
From playlist Probability and Statistics
Mod-01 Lec-24 Model Parameter Estimation using Gauss-Newton Method
Advanced Numerical Analysis by Prof. Sachin C. Patwardhan,Department of Chemical Engineering,IIT Bombay.For more details on NPTEL visit http://nptel.ac.in
From playlist IIT Bombay: Advanced Numerical Analysis | CosmoLearning.org
Score estimation with infinite-dimensional exponential families – Dougal Sutherland, UCL
Many problems in science and engineering involve an underlying unknown complex process that depends on a large number of parameters. The goal in many applications is to reconstruct, or learn, the unknown process given some direct or indirect observations. Mathematically, such a problem can
From playlist Approximating high dimensional functions
Linear Approximation & the Tangent Planes & the Differential: More Depth
Multivariable calculus lecture focusing on Linear Approximation & the Tangent Planes & the Differential
From playlist Multivariable Derivatives