Convolution in the time domain
Now that you understand the Fourier transform, it's time to start learning about time-frequency analyses. Convolution is one of the best ways to extract time-frequency dynamics from a time series. Convolution can be conceptualized and implemented in the time domain or in the frequency doma
From playlist OLD ANTS #3) Time-frequency analysis via Morlet wavelet convolution
Stack Data Structure - Algorithm
This is an explanation of the dynamic data structure known as a stack. It includes an explanation of how a stack works, along with pseudocode for implementing the push and pop operations with a static array variable.
From playlist Data Structures
Clustering Introduction - Practical Machine Learning Tutorial with Python p.34
In this tutorial, we shift gears and introduce the concept of clustering. Clustering is form of unsupervised machine learning, where the machine automatically determines the grouping for data. There are two major forms of clustering: Flat and Hierarchical. Flat clustering allows the scient
From playlist Machine Learning with Python
Reactive Systems use a high-performance software architecture. They are resilient under stress, and their reactive design allows them to scale elastically to meet demand. The reactive design approach allows the creation of more complex, more flexible systems and forms the basis for some of
From playlist Software Engineering
Gilles Pagès: Optimal vector Quantization: from signal processing to clustering and ...
Abstract: Optimal vector quantization has been originally introduced in Signal processing as a discretization method of random signals, leading to an optimal trade-off between the speed of transmission and the quality of the transmitted signal. In machine learning, similar methods applied
From playlist Probability and Statistics
From playlist Hierarchical Clustering
Stanford Seminar - How Behavior Spreads
EE380: Computer Systems Colloquium Seminar "How Behavior Spreads" Speaker: Damon Centola, University of Pennsylvania About the talk: New social movements, technologies, and public-health initiatives often struggle to take off, yet many diseases disperse rapidly without issue. Can the les
From playlist Stanford EE380-Colloquium on Computer Systems - Seminar Series
Random Processes and Stationarity
http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. Introduction to describing random processes using first and second moments (mean and autocorrelation/autocovariance). Definition of a stationa
From playlist Random Signal Characterization
Stanford Seminar - Affect, Affordances and the Psychology of Social Media
Jeffrey Hancock Stanford University This seminar series features dynamic professionals sharing their industry experience and cutting edge research within the human-computer interaction (HCI) field. Each week, a unique collection of technologists, artists, designers, and activists will dis
From playlist Stanford Seminars
The Maths of Contagion: Why Things Spread and Why They Stop - with Adam Kucharski
Mathematical models can investigate the spread of diseases such as malaria or Covid-19, but also ideas, misinformation and internet memes. Adam's book "The Rules of Contagion" is available now on Amazon: https://geni.us/QIdazc Why are some diseases predictable, and others swamped in uncer
From playlist Livestreams
Clustering (2): Hierarchical Agglomerative Clustering
Hierarchical agglomerative clustering, or linkage clustering. Procedure, complexity analysis, and cluster dissimilarity measures including single linkage, complete linkage, and others.
From playlist cs273a
Queue Data Structure – Algorithms
This is an explanation of the dynamic data structure known as a queue. It compares a linear queue implemented by means of a dynamic array with a linear queue implemented with a static array. It also includes an explanation of how a circular queue works, along with pseudocode for the enqu
From playlist Data Structures
Why Humans Have Friends: The Evolutionary Biology of Lifelong Social Interactions
Nicholas Christakis, MD, PhD, Sol Goldman Family Professor of Social & Natural Science and Professor of Internal Medicine/General Medicine, gives a lecture entitled, "Why Humans Have Friends: The Evolutionary Biology of Lifelong Social Interactions," during the Yale Claude D. Pepper Sympos
From playlist Aging Research at Yale: Past, Present and Future
In the Company of Scholars Lecture Series: "Using Social Networks for Good"
Human beings choose their friends, neighbors, and co-workers, and we inherit our relatives; and each of the people to whom we are connected also does the same, such that, in the end, we humans assemble ourselves into vast, face-to-face social networks. These networks are the human equivale
From playlist In The Company Of Scholars
From playlist Guest Speakers
Claudia Ceci: Optimal reinsurance via BSDEs in a partially observable contagion model
HYBRID EVENT Recorded during the meeting "Advances in Stochastic Control and Optimal Stopping with Applications in Economics and Finance" the September 12, 2022 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video a
From playlist Probability and Statistics
Strong community organization of populations can promote ... by Sitabhra Sinha (Part 1)
Games, Epidemics and Behavior URL: http://www.icts.res.in/discussion_meeting/geb2016/ DATES: Monday 27 Jun, 2016 - Friday 01 Jul, 2016 VENUE : Madhava lecture hall, ICTS Bangalore DESCRIPTION: The two main goals of this Discussion Meeting are: 1. To explore the foundations of policy d
From playlist Games, Epidemics and Behavior
Edriss Titi: Determining the global dynamics of the two-dimensional Navier-Stokes equations by...
One of the main characteristics of infinite-dimensional dissipative evolution equations, such as the Navier-Stokes equations and reaction-diffusion systems, is that their long-time dynamics is determined by finitely many parameters – finite number of determining modes, nodes, volume elemen
From playlist Analysis and its Applications
Network Analysis. Lecture 14. Social contagion and spread of information.
Information diffusion. Rumor spreading models. Homogenous and mean field models. Examples. Cascades and information propagation trees. Lecture slides: http://www.leonidzhukov.net/hse/2015/networks/lectures/lecture14.pdf
From playlist Structural Analysis and Visualization of Networks.