Math for Liberal Studies: Plurality and Majority
In this video, we practice finding the plurality winner of an election, and determine whether or not that winner received a majority. For more info, visit the Math for Liberal Studies homepage: http://webspace.ship.edu/jehamb/mls/index.html
From playlist Math for Liberal Studies
8 2 Randomized Selection Analysis 21 min
From playlist Algorithms 1
Randomized Greedy Algorithms for the Maximum Matching Problem with New Analysis - Mario Szegedy
Mario Szegedy Rutgers, The State University of New Jersey April 30, 2012 http://math.ias.edu/files/seminars/Szeg.pdf For more videos, visit http://video.ias.edu
From playlist Mathematics
Random Oracle - Applied Cryptography
This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
From playlist Applied Cryptography
Introduction to Weighted Voting
The video provided an introduction to weighted voting. Short hand notation is discusses as well as the definitions of a dictactor, veto power, and dummy players. Site: http://mathispower4u
From playlist Weighted Voting
Randomness Quiz - Applied Cryptography
This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
From playlist Applied Cryptography
Math for Liberal Studies - Lecture 2.8.1 Weighted Voting Systems
This is the first video for Math for Liberal Studies Section 2.8: Weighted Voting Systems. In this video, I talk about the basic definitions and notation for weighted voting systems. In these systems, the voters are treated unequally. This may seem unfair, but there are many real-world exa
From playlist Math for Liberal Studies Lectures
Strongly log concave polynomials...Bases of Matroids - Shayan Oveis Gharan
More videos on http://video.ias.edu
From playlist Mathematics
Voting Theory: Plurality Method and Condorcet Criterion
This video explains how to determine the winner of an election using the plurality methods and how to determine the Condorcet winner. Site: http://mathispower4u.com
From playlist Voting Theory
New Pseudo-deterministic Algorithms - Shafi Goldwasser
A Celebration of Mathematics and Computer Science Celebrating Avi Wigderson's 60th Birthday October 5 - 8, 2016 More videos on http://video.ias.edu
From playlist Mathematics
Fractionally Log-Concave and Sector-Stable Polynomials by Nima Anari
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)
Deep Learning Crash Course for Beginners
Learn the fundamental concepts and terminology of Deep Learning, a sub-branch of Machine Learning. This course is designed for absolute beginners with no experience in programming. You will learn the key ideas behind deep learning without any code. You'll learn about Neural Networks, Mach
From playlist Machine Learning
Stochastic Gradient Descent: where optimization meets machine learning- Rachel Ward
2022 Program for Women and Mathematics: The Mathematics of Machine Learning Topic: Stochastic Gradient Descent: where optimization meets machine learning Speaker: Rachel Ward Affiliation: University of Texas, Austin Date: May 26, 2022 Stochastic Gradient Descent (SGD) is the de facto op
From playlist Mathematics
Nexus Trimester - Sewoong Oh (UIUC)
Near-optimal message-passing algorithms for crowdsourcing Sewoong Oh (UIUC) March 17, 2016 Abstract: Crowdsourcing systems, like Amazon Mechanical Turk, provide platforms where large-scale projects are broken into small tasks that are electronically distributed to numerous on-demand cont
From playlist 2016-T1 - Nexus of Information and Computation Theory - CEB Trimester
TDLS - Classics: SMOTE, Synthetic Minority Over-sampling Technique (algorithm)
Toronto Deep Learning Series, 26 November 2018 Paper: https://arxiv.org/pdf/1106.1813.pdf Speaker: Jason Grunhut (Telus Digital) Host: Telus Digital Date: Nov 26th, 2018 SMOTE: Synthetic Minority Over-sampling Technique An approach to the construction of classifiers from imbalanced da
From playlist Math and Foundations
Kharkov, Universal approach to β-matrix models - Valerie King
Valerie King University of Victoria; Member, School of Mathematics April 1, 2014
From playlist Mathematics
Statistical mechanics of deep learning by Surya Ganguli
Statistical Physics Methods in Machine Learning DATE: 26 December 2017 to 30 December 2017 VENUE: Ramanujan Lecture Hall, ICTS, Bengaluru The theme of this Discussion Meeting is the analysis of distributed/networked algorithms in machine learning and theoretical computer science in the
From playlist Statistical Physics Methods in Machine Learning
Math for Liberal Studies - Lecture 2.1.2 Plurality and Majority
This is the second video lecture for Math for Liberal Studies Section 2.1: Controversial Elections. In this video, I define the terms "plurality" and "majority," and discuss the "plurality method" for determining the winner of an election.
From playlist Math for Liberal Studies Lectures
Optimal Weak to Strong Learning - Kasper Green Larsen
Computer Science/Discrete Mathematics Seminar I Topic: Optimal Weak to Strong Learning Speaker: Kasper Green Larsen Affiliation: Aarhus University Date: December 05, 2022 The classic algorithm AdaBoost allows to convert a weak learner, that is an algorithm that produces a hypothesis whic
From playlist Mathematics