Jensen's inequality

Jensen's Inequality : Data Science Basics

a surprisingly super useful result for data science! 0:00 Convex Functions 3:54 Jensen's Inequality 8:40 Application

From playlist Data Science Basics

S18.2 Jensen's Inequality

MIT RES.6-012 Introduction to Probability, Spring 2018 View the complete course: https://ocw.mit.edu/RES-6-012S18 Instructor: John Tsitsiklis License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu

From playlist MIT RES.6-012 Introduction to Probability, Spring 2018

Gronwall inequality

In this video, I state and prove Grönwall’s inequality, which is used for example to show that (under certain assumptions), ODEs have a unique solution. Basically it says that if a function satisfies a differential equation, but with an inequality, then it must grow sub-exponentially.

From playlist Real Analysis

Joe Neeman: Gaussian isoperimetry and related topics II

The Gaussian isoperimetric inequality gives a sharp lower bound on the Gaussian surface area of any set in terms of its Gaussian measure. Its dimension-independent nature makes it a powerful tool for proving concentration inequalities in high dimensions. We will explore several consequence

From playlist Winter School on the Interplay between High-Dimensional Geometry and Probability

Inequality Practice

practice with difference inequalities

From playlist Geometry

Joe Neeman: Gaussian isoperimetry and related topics I

The Gaussian isoperimetric inequality gives a sharp lower bound on the Gaussian surface area of any set in terms of its Gaussian measure. Its dimension-independent nature makes it a powerful tool for proving concentration inequalities in high dimensions. We will explore several consequence

From playlist Winter School on the Interplay between High-Dimensional Geometry and Probability

Joe Neeman: Gaussian isoperimetry and related topics III

The Gaussian isoperimetric inequality gives a sharp lower bound on the Gaussian surface area of any set in terms of its Gaussian measure. Its dimension-independent nature makes it a powerful tool for proving concentration inequalities in high dimensions. We will explore several consequence

From playlist Winter School on the Interplay between High-Dimensional Geometry and Probability

Calculus - The Fundamental Theorem, Part 1

The Fundamental Theorem of Calculus. First video in a short series on the topic. The theorem is stated and two simple examples are worked.

From playlist Calculus - The Fundamental Theorem of Calculus

Stanford CS229: Machine Learning | Summer 2019 | Lecture 16 - K-means, GMM, and EM

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3njDenA Anand Avati Computer Science, PhD To follow along with the course schedule and syllabus, visit: http://cs229.stanford.edu/syllabus-summer2019.html

From playlist Stanford CS229: Machine Learning Course | Summer 2019 (Anand Avati)

Multivariable Calculus | The Squeeze Theorem

We calculate a limit using a multivariable version of the squeeze theorem. http://www.michael-penn.net http://www.randolphcollege.edu/mathematics/

From playlist Multivariable Calculus

Joe Neeman: rho convexity and Ehrhard's inequality

We say that a function of two real variables is rho-convex if its Hessian matrix, multiplied by rho on the off-diagonal, is positive semi-definite. This notion (and its generalization to functions of more than two variables) turns out to give simple proofs of various inequalities on Gaussi

From playlist HIM Lectures: Follow-up Workshop to JTP "Optimal Transportation"

Lecture 14 - Expectation-Maximization Algorithms | Stanford CS229: Machine Learning (Autumn 2018)

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3G6tSE6 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

Lecture 12 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses unsupervised learning in the context of clustering, Jensen's inequality, mixture of Gaussians, and expectation-maximization. This course provides a broad in

From playlist Lecture Collection | Machine Learning

Karthik Chandrasekaran: lp-Norm Multiway Cut

In lp-norm multiway cut, the input is an undirected graph with non-negative edge weights along with k terminals and the goal is to find a partition of the vertex set into k parts each containing exactly one terminal so as to minimize the lp-norm of the cut values of the parts. This is a un

From playlist Workshop: Approximation and Relaxation

Convexity and risk premium impacts on shape of term structure (FRM T5-08)

In this video, I'm going to try to illustrate all of the important ideas that are in Tuckman's Chapter 8: The Evolution of Short Rates and the Shape of the Term Structure. This chapter discusses the shape of the term structure and the key influences on the shape of the spot rate term struc

From playlist Market Risk (FRM Topic 5)

Lec 9 | MIT 6.046J / 18.410J Introduction to Algorithms (SMA 5503), Fall 2005

Lecture 09: Relation of BSTs to Quicksort | Analysis of Random BST View the complete course at: http://ocw.mit.edu/6-046JF05 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu

From playlist MIT 6.046J / 18.410J Introduction to Algorithms (SMA 5503),

Algebra - Ch. 3: Formula, Inequalities, Absolute Value (16 of 33) What is a Linear Inequality? 1

Visit http://ilectureonline.com for more math and science lectures! In this video I will explain what is a linear inequality (“less than” or “greater than”) and show 3 examples of 2 different ways to express the same inequality and how to graphically express that inequality. (Part 1) To

From playlist ALGEBRA CH 3 FORMULAS, INEQUALITIES, ABSOLUTE VALUES

The Financial Economy: Where It Came From and What Might Come Next - Nicholas Lemann

Lecture transcript available here: https://bit.ly/38oP09D

From playlist Social Science