What are Irregular Graphs? (and why they are boring) | Graph Theory
What are irregular graphs? After learning about regular graphs, this is a natural question to ask. Irregular graphs are the opposite of regular graphs, which means that irregular graphs are graphs in which all vertices have distinct degrees. Equivalently, a graph is irregular if and only i
From playlist Graph Theory
Randomly Generated Graphs - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
Spectral Geometry of Random Graphs - Igor Rivin
Igor Rivin Temple University; Member, School of Mathematics October 20, 2010 we will describe various models of sparse and planar graphs and the associated distributions of eigenvalues (and eigenvalue spacings) which come up. The talk will be light on theorems, and heavy on experimental da
From playlist Mathematics
This is How You Use the Chain Rule in Calculus
This is How You Use the Chain Rule in Calculus
From playlist Random calculus problems:)
The Derivative of a Constant Example with y = 2
The Derivative of a Constant Example with y = 2
From playlist Random calculus problems:)
The Most Powerful Tool Based Entirely On Randomness
We see the effects of randomness all around us on a day to day basis. In this video we’ll be discussing a couple of different techniques that scientists use to understand randomness, as well as how we can harness its power. Basically, we'll study the mathematics of randomness. The branch
From playlist Classical Physics by Parth G
Lecture 2. Power law and scale-free networks.
Network Science 2021 @ HSE http://www.leonidzhukov.net/hse/2021/networks/
From playlist Network Science, 2021
Modeling limits - P. Ossona de Mendez - Workshop 1 - CEB T1 2018
Patrice Ossona de Mendez (EHSS) / 30.01.2018 A sequence of graphs is FO-convergent if the probability of satisfaction of every first-order formula converges. A graph modeling is a graph, whose domain is a standard probability space, with the property that every definable set is Borel. It
From playlist 2018 - T1 - Model Theory, Combinatorics and Valued fields
This is part of a series of lectures on the Zermelo-Fraenkel axioms for set theory. We discuss the axiom of infinity, and give some examples of models where it does not hold. For the other lectures in the course see https://www.youtube.com/playlist?list=PL8yHsr3EFj52EKVgPi-p50fRP2_SbG
From playlist Zermelo Fraenkel axioms
What is a Highly Irregular Graph? | Locally Irregular Graph, Graph Theory
Irregular graphs are a bit tricky to define, because the most intuitive definition leads to nothing of interest. In today's math video lesson, we introduce an alternative definition of irregular graph, with plenty of examples, called a highly irregular graph! These graphs are also sometime
From playlist Graph Theory
Lecture 6. Structural properties of networks
Network Science 2021 @ HSE http://www.leonidzhukov.net/hse/2021/networks/
From playlist Network Science, 2021
Christian Borgs: Graphons and graphexes as limits of sparse graphs - lecture 2
Abstract: Graphons and graphexes are limits of graphs which allow us to model and estimate properties of large-scale networks. In this pair of talks, we review the theory of dense graph limits, and give two alterative theories for limits of sparse graphs - one leading to unbounded graphons
From playlist Combinatorics
The Abel lectures: László Lovász and Avi Wigderson
0:30 Introduction by the Abel Prize Committee Chair, Hans Munthe-Kaas 02:42 László Lovász: Continuous limits of finite structures 49:27 Questions and answers 1:00:31 Avi Wigderson: The Value of Errors in Proofs 1:41:24 Questions and answers 1:50:20 Final remarks by John Grue, Chair of the
From playlist Abel Lectures
Classical and Quantum Subjectivity
Uncertainty is a major component of subjective logic beliefs. We discuss the cloud of uncertainty across Markov networks, insights from computational irreducibility, and negative quantum quasiprobabilities and beliefs.
From playlist Wolfram Technology Conference 2022
High dimensional expanders – Alexander Lubotzky – ICM2018
Plenary Lecture 13 High dimensional expanders Alexander Lubotzky Abstract: Expander graphs have been, during the last five decades, the subject of a most fruitful interaction between pure mathematics and computer science, with influence and applications going both ways. In the last decad
From playlist Plenary Lectures
Wolfram Physics Project: Philosophical Implications & Q&A
Stephen Wolfram & Jonathan Gorard continue answering questions about the new Wolfram Physics Project, this time specifically for Philosophy implications. Begins at 3:45 Originally livestreamed at: https://twitch.tv/stephen_wolfram Stay up-to-date on this project by visiting our website:
From playlist Wolfram Physics Project Livestream Archive
On the Number of Hamilton Cycles in Psdueo-Random Graphs - Michael Krivelevich
Michael Krivelevich Tel Aviv University October 17, 2011 A pseudo-random graph is a graph G resembling a typical random graph of the same edge density. Pseudo-random graphs are expected naturally to share many properties of their random counterparts. In particular, many of their enumerati
From playlist Mathematics
27. Backpropagation: Find Partial Derivatives
MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018 Instructor: Gilbert Strang View the complete course: https://ocw.mit.edu/18-065S18 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63oMNUHXqIUcrkS2PivhN3k In this lecture, P
From playlist MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018