Set Theory (Part 2): ZFC Axioms
Please feel free to leave comments/questions on the video and practice problems below! In this video, I introduce some common axioms in set theory using the Zermelo-Fraenkel w/ choice (ZFC) system. Five out of nine ZFC axioms are covered and the remaining four will be introduced in their
From playlist Set Theory by Mathoma
What is the Roster Method? (Roster Form) | Set Theory, Writing Sets, Expressing Sets
The roster method is one of several set notations you can use to write a set. It is perhaps the easiest understand, but is only useful for writing out sets when they are finite and small in size, or if they are dictated by an easy to describe pattern (that is finite or infinite). If you ar
From playlist Set Theory
Power Set of the Power Set of the Power Set of the Empty Set | Set Theory
The power set of the power set of the power set of the empty set, we'll go over how to find just that in today's set theory video lesson! We'll also go over the power set of the empty set, the power set of the power set of the empty set, and we'll se the power set of the power set of the p
From playlist Set Theory
Dominating Sets and Domination Number of Graphs | Graph Theory
A vertex is said to dominate itself and its neighbors. Then, a dominating set of a graph G is a vertex subset S of G such that every vertex in G is dominated by some vertex in S. This means every vertex in G-S is adjacent to some vertex in S. A dominating set of minimum cardinality is a mi
From playlist Graph Theory
Listing Subsets Using Tree Diagrams | Set Theory, Subsets, Power Sets
Here is a method for completely listing the subsets of a given set using tree diagrams. It's a handy way to make sure you don't miss any subsets when trying to find them. It's not super efficient, but it is reliable! The process is pretty simple, we begin with the empty set, and then branc
From playlist Set Theory
How to Identify the Elements of a Set | Set Theory
Sets contain elements, and sometimes those elements are sets, intervals, ordered pairs or sequences, or a slew of other objects! When a set is written in roster form, its elements are separated by commas, but some elements may have commas of their own, making it a little difficult at times
From playlist Set Theory
(ML 11.8) Bayesian decision theory
Choosing an optimal decision rule under a Bayesian model. An informal discussion of Bayes rules, generalized Bayes rules, and the complete class theorems.
From playlist Machine Learning
We give some basic definitions and notions associated with sets. In particular, we describe sets via the "roster method", via a verbal description, and with set-builder notation. We also give an example of proving the equality of two sets. Please Subscribe: https://www.youtube.com/michael
From playlist Proof Writing
Mod-01 Lec-09 Introduction to Nanomaterials
Nanostructures and Nanomaterials: Characterization and Properties by Characterization and Properties by Dr. Kantesh Balani & Dr. Anandh Subramaniam,Department of Nanotechnology,IIT Kanpur.For more details on NPTEL visit http://nptel.ac.in.
From playlist IIT Kanpur: Nanostructures and Nanomaterials | CosmoLearning.org
Yanghui Liu (Baruch College) -- Numerical approximations for rough differential equations
The rough paths theory provides a general framework for stochastic differential equations driven by processes with very low regularities, which has important applications in finance, statistical mechanics, hydro-dynamics and so on. The numerical approximation is a crucial step while applyi
From playlist Columbia SPDE Seminar
AMMI 2022 Course "Geometric Deep Learning" - Seminar 1 (Physics-based GNNs) - Francesco Di Giovanni
Video recording of the course "Geometric Deep Learning" taught in the African Master in Machine Intelligence in July 2022 Seminar 1 - Graph neural networks through the lens of multi-particle dynamics and gradient flows - Francesco Di Giovanni (Twitter) Slides: https://www.dropbox.com/s/
From playlist AMMI Geometric Deep Learning Course - Second Edition (2022)
Bound-preserving numerical solutions of variable density two-phase flows
Date and Time: Thursday, November 11, 12:00pm Eastern time zone Speaker: Beatrice Riviere, Rice University Abstract: Modeling pore-scale flows modeling is important for many applications relevant to energy and environment. Phase-field models are popular models because they implicitly tra
From playlist SIAM Geosciences Webinar Series
Lecture 9: GNNs as Dynamic Systems - Francesco Di Giovanni
Video recording of the First Italian School on Geometric Deep Learning held in Pescara in July 2022. Slides: https://www.sci.unich.it/geodeep2022/slides/GRAFF_presentation%20(17).pdf
From playlist First Italian School on Geometric Deep Learning - Pescara 2022
Geordie Williamson: Geometric Representation Theory and the Geometric Satake Equivalence
MSI Virtual Colloquium: Geometric Representation Theory and the Geometric Satake Equivalence Geordie Williamson (University of Sydney) During this colloquium Geordie will explain in very broad terms, what the Langlands correspondence is and why people care about it. He will then explain i
From playlist Geordie Williamson: Representation theory and the Geometric Satake
Total variation denoising with iterated conditional expectation - Louchet - Workshop 2 - CEB T1 2019
Cécile Louchet (Univ. Orléans) / 12.03.2019 Total variation denoising with iterated conditional expectation. Imaging tasks most often require an energy minimization interpretable in a probabilistic approach as a maximum a posteriori. Taking instead the expectation a posteriori gives an
From playlist 2019 - T1 - The Mathematics of Imaging
Set-Roster vs Set-Builder notation
Learning Objectives: 1) Write a set with infinitely many elements using Set-Roster notation 2) Write a set using Set-Builder notation 3) Convert between these two different notations for sets. **************************************************** YOUR TURN! Learning math requires more tha
From playlist Discrete Math (Full Course: Sets, Logic, Proofs, Probability, Graph Theory, etc)
Paolo Boldi - Axioms for centrality: rank monotonicity for PageRank
https://indico.math.cnrs.fr/event/3475/attachments/2180/2562/Boldi_GomaxSlides.pdf
From playlist Google matrix: fundamentals, applications and beyond
DDPS | Neural Galerkin schemes with active learning for high-dimensional evolution equations
Title: Neural Galerkin schemes with active learning for high-dimensional evolution equations Speaker: Benjamin Peherstorfer (New York University) Description: Fitting parameters of machine learning models such as deep networks typically requires accurately estimating the population loss
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
The integral coefficient geometric Satake equivalence in mixed characteristic - Jize Yu
Virtual Workshop on Recent Developments in Geometric Representation Theory Topic: The integral coefficient geometric Satake equivalence in mixed characteristic Speaker: Jize Yu Affiliation: Member, School of Mathematics Date: November 16, 2020 For more video please visit http://video.ias
From playlist Virtual Workshop on Recent Developments in Geometric Representation Theory
Quentin Berthet: Learning with differentiable perturbed optimizers
Machine learning pipelines often rely on optimization procedures to make discrete decisions (e.g. sorting, picking closest neighbors, finding shortest paths or optimal matchings). Although these discrete decisions are easily computed in a forward manner, they cannot be used to modify model
From playlist Control Theory and Optimization