Rainer von Sachs: Time-frequency analysis of locally stationary Hawkes processes
Abstract : In this talk we address generalisation of stationary Hawkes processes in order to allow for a time-evolutive second-order analysis. A formal derivation of a time-frequency analysis via a time-varying Bartlett spectrum is given by introduction of the new class of locally stationa
From playlist Probability and Statistics
Introduction to Hyperbolic Functions
This video provides a basic overview of hyperbolic function. The lesson defines the hyperbolic functions, shows the graphs of the hyperbolic functions, and gives the properties of hyperbolic functions.
From playlist Using the Properties of Hyperbolic Functions
HTE: Confounding-Robust Estimation
Professor Stefan Wager discusses general principles for the design of robust, machine learning-based algorithms for treatment heterogeneity in observational studies, as well as the application of these principles to design more robust causal forests (as implemented in GRF).
From playlist Machine Learning & Causal Inference: A Short Course
Sylvia Richardson: Exploring the presence of complex dependence structures in epidemiological...
Abstract: Faced with data containing a large number of inter-related explanatory variables, finding ways to investigate complex multi-factorial effects is an important statistical task. This is particularly relevant for epidemiological study designs where large numbers of covariates are ty
From playlist Probability and Statistics
Introduction to Hyperbolic Functions
This video provides a basic overview of hyperbolic function. The lesson defines the hyperbolic functions, shows the graphs of the hyperbolic functions, and gives the properties of hyperbolic functions. Site: http://mathispower4u.com Blog: http://mathispower4u.wordpress.com
From playlist Differentiation of Hyperbolic Functions
Suhasini Subba Rao: Fourier based methods for spatial data observed on irregularly spaced locations
Abstract : In this talk we introduce a class of statistics for spatial data that is observed on an irregular set of locations. Our aim is to obtain a unified framework for inference and the statistics we consider include both parametric and nonparametric estimators of the spatial covarianc
From playlist Probability and Statistics
Linear regression is used to compare sets or pairs of numerical data points. We use it to find a correlation between variables.
From playlist Learning medical statistics with python and Jupyter notebooks
What are Hyperbolas? | Ch 1, Hyperbolic Trigonometry
This is the first chapter in a series about hyperbolas from first principles, reimagining trigonometry using hyperbolas instead of circles. This first chapter defines hyperbolas and hyperbolic relationships and sets some foreshadowings for later chapters This is my completed submission t
From playlist Summer of Math Exposition 2 videos
What is the difference between convex and concave
👉 Learn about polygons and how to classify them. A polygon is a plane shape bounded by a finite chain of straight lines. A polygon can be concave or convex and it can also be regular or irregular. A concave polygon is a polygon in which at least one of its interior angles is greater than 1
From playlist Classify Polygons
What are four types of polygons
👉 Learn about polygons and how to classify them. A polygon is a plane shape bounded by a finite chain of straight lines. A polygon can be concave or convex and it can also be regular or irregular. A concave polygon is a polygon in which at least one of its interior angles is greater than 1
From playlist Classify Polygons
Using a set of points determine if the figure is a parallelogram using the midpoint formula
👉 Learn how to determine the figure given four points. A quadrilateral is a polygon with four sides. Some of the types of quadrilaterals are: parallelogram, square, rectangle, rhombus, kite, trapezoid, etc. Each of the types of quadrilateral has its properties. Given four points that repr
From playlist Quadrilaterals on a Coordinate Plane
Nikolaus Kriegskorte - Controversial stimuli: experiments to adjudicate computational hypotheses
Recorded 13 January 2023. Nikolaus Kriegeskorte of Columbia University presents "Controversial stimuli: Optimizing experiments to adjudicate among computational hypotheses" at IPAM's Explainable AI for the Sciences: Towards Novel Insights Workshop. Learn more online at: http://www.ipam.ucl
From playlist 2023 Explainable AI for the Sciences: Towards Novel Insights
Using & Expanding the NLP Models Hub 1 | Webinar
Spark NLP and Spark OCR Free Trials are available here: https://www.johnsnowlabs.com/spark-nlp-try-free/ The NLP Models Hub which powers the Spark NLP and NLU libraries takes a different approach than the hubs of other libraries like TensorFlow, PyTorch, and Hugging Face. While it also pr
From playlist AI & NLP Webinars
Learn about HEV modeling and simulation. In this video, you will: - Learn about different methods for creating HEV component models. - See how Powertrain Blocksetâ„¢ and Simscapeâ„¢ tools can be used for HEV modeling. - Learn best practices for getting started and creating new plant models.
From playlist Hybrid Electric Vehicles
DDPS | Learning hierarchies of reduced-dimension and context-aware models for Monte Carlo sampling
In this DDPS Seminar Series talk from Sept. 2, 2021, University of Texas at Austin postdoctoral fellow Ionut-Gabriel Farcas discusses hierarchies of reduced-dimension and context-aware low-fidelity models for multi-fidelity Monte Carlo sampling. Description: In traditional model reduction
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
This is Lecture 14 of the CSE519 (Data Science) course taught by Professor Steven Skiena [http://www.cs.stonybrook.edu/~skiena/] at Stony Brook University in 2016. The lecture slides are available at: http://www.cs.stonybrook.edu/~skiena/519 More information may be found here: http://www
From playlist CSE519 - Data Science Fall 2016
Stanford CS330: Deep Multi-task & Meta Learning I 2021 I Lecture 15
For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai To follow along with the course, visit: http://cs330.stanford.edu/fall2021/index.html To view all online courses and programs offered by Stanford, visit: http:/
From playlist Stanford CS330: Deep Multi-Task & Meta Learning I Autumn 2021I Professor Chelsea Finn
What are the names of different types of polygons based on the number of sides
👉 Learn about polygons and how to classify them. A polygon is a plane shape bounded by a finite chain of straight lines. A polygon can be concave or convex and it can also be regular or irregular. A concave polygon is a polygon in which at least one of its interior angles is greater than 1
From playlist Classify Polygons