What Can't We Predict With Math?
Solar eclipses are fairly predictable, but the behavior of the stock market over the next couple days...not so much. But why? Is any given problem simply a matter of having a big enough computer and a complex enough algorithm to solve it, or are there certain things that lie beyond the rea
From playlist Technology
Prediction by the Numbers Sneak Peek
Discover why some predictions succeed and others fail as experts forecast the future. Premiering February 28, 2018 at 9 pm on PBS NOVA on Facebook: https://www.facebook.com/NOVAonline NOVA on Twitter: @novapbs NOVA on Instagram: @novapbs
From playlist Previews
Can We Predict the Future of Aperiodic Sources? - Matthew Graham - 6/26/2019
AstroInformatics 2019 Conference: AstroInformatics for Large Projects http://astroinformatics2019.org/
From playlist AstroInformatics 2019 Conference
Likelihood Estimation - THE MATH YOU SHOULD KNOW!
Likelihood is a confusing term. It is not a probability, but is proportional to a probability. Likelihood and probability can't be used interchangeably. In this post, we will be dissecting the likelihood as a concept and understand why likelihood is important in machine learning. We will a
From playlist The Math You Should Know
Paul Davies - What Can't Be Predicted in Physics?
Prediction is the fruitful product of good science, but how far can prediction go? Physics is the most mathematical and rigorous of the sciences and so prediction is most successful in physics. But are there limits to predictability in physics? What about quantum indeterminacy? Are there u
From playlist Closer To Truth - Paul Davies Interviews
Counting Outcomes (Size of Sample Space)
More resources available at www.misterwootube.com
From playlist Relative Frequency and Probability
Nonautonomous and Random Dynamical Systems Into the Climate Sciences - Ghil -Workshop 1 -CEB T3 2019
Ghil (ENS, Paris, and UCLA) / 09.10.2019 Nonautonomous and Random Dynamical Systems Into the Climate Sciences H. Poincaré already raised doubts about the predictability of weather due to the divergence of orbits of dynamical systems associated more recently with chaos. Progress in th
From playlist 2019 - T3 - The Mathematics of Climate and the Environment
Conditional Probability: Bayes’ Theorem – Disease Testing (Table and Formula)
This video shows how to determine conditional probability using a table and using Bayes' theorem. @mathipower4u
From playlist Probability
Probabilistic Forecasting in TimeSeries
More info + to join: https://community.ai.science/time-series-forecasting
From playlist Mega Meetup VIII
Model Predictive Control Design Parameters | Understanding MPC, Part 3
To successfully control a system using an MPC controller, you need to carefully select its design parameters. - Model Predictive Control Toolbox: http://bit.ly/2xgwWvN - What Is Model Predictive Control Toolbox?: http://bit.ly/2xfEe2M - Design Controller Using MPC Designer: http://bit.ly/
From playlist Understanding Model Predictive Control
Finite-Horizon, Energy-Optimal Trajectories in Unsteady Flows
This video by Kartik Krishna investigates the use of finite-horizon model predictive control (MPC) for the energy-efficient trajectory planning of an active mobile sensor in an unsteady fluid flow. Connections between the finite-time optimal trajectories and finite-time Lyapunov exponents
From playlist Research Abstracts from Brunton Lab
ETA Prediction with Graph Neural Networks in Google Maps | Paper Explained
👨👩👧👦 JOIN OUR DISCORD COMMUNITY: Discord ► https://discord.gg/peBrCpheKE 📢 SUBSCRIBE TO MY MONTHLY AI NEWSLETTER: Substack ► https://aiepiphany.substack.com/ ❤️ Become The AI Epiphany Patreon ❤️ ► https://www.patreon.com/theaiepiphany In this video I cover the "ETA Prediction with Gr
From playlist Graph Neural Nets
sktime - A Unified Toolbox for ML with Time Series
This tutorial is about sktime - a unified framework for machine learning with time series. sktime features various time series algorithms and modular tools for pipelining, ensembling and tuning. You will learn how to use, combine and evaluate different algorithms on real-world data sets an
From playlist Python
Black Holes | History and Philosophy of Astronomy 8.05
Learn about the history and philosophy of astronomy from Professor Impey, a University Distinguished Professor of Astronomy at the University of Arizona, with our Knowing the Universe: History and Philosophy of Astronomy course here on YouTube. This video is part of module 8, Relativity.
From playlist History and Philosophy Course Module 8: Relativity
Stephen Hawking - Quantum Black Holes
In the 1960s Roger Penrose and Stephen Hawking produced their ground-breaking work on Black Holes. In 2017 Stephen gave the first Oxford Mathematics Roger Penrose Public Lecture in honour of his great friend. His subject? Black Holes of course. The full lecture, one of Stephen's last, is n
From playlist Oxford Mathematics Public Lectures
Lecture 9 | Topics in String Theory
(March 14, 2011) Leonard Susskind gives a lecture on string theory and particle physics that focuses on the mechanisms that make the universe hot. In the last of course of this series, Leonard Susskind continues his exploration of string theory that attempts to reconcile quantum mechanics
From playlist Lecture Collection | Topics in String Theory (Winter 2011)
Relativity 11d - spherical bodies and black holes IV
Relativity playlist: http://www.youtube.com/playlist?list=PLF56602BAC693237E Appendix video: https://youtu.be/MDAmsOrFWp4 Leonard Susskind on black hole horizons: https://youtu.be/yMRYZMv0jRE
From playlist Relativity
What is Model Predictive Control? | Understanding MPC, Part 2
Learn how model predictive control (MPC) works. - Model Predictive Control Toolbox: http://bit.ly/2xgwWvN - What Is Model Predictive Control Toolbox?: http://bit.ly/2xfEe2M - Getting Started with Model Predictive Control Toolbox: http://bit.ly/2GskEY4 MPC uses a model of the plant to mak
From playlist Understanding Model Predictive Control
Status of Hubble Tension by Vivian Poulin
PROGRAM LESS TRAVELLED PATH TO THE DARK UNIVERSE ORGANIZERS: Arka Banerjee (IISER Pune), Subinoy Das (IIA, Bangalore), Koushik Dutta (IISER, Kolkata), Raghavan Rangarajan (Ahmedabad University) and Vikram Rentala (IIT Bombay) DATE & TIME: 13 March 2023 to 24 March 2023 VENUE: Ramanujan
From playlist LESS TRAVELLED PATH TO THE DARK UNIVERSE
Climate Sensitivity and Some Speculative Implications for Modeling Catastrophes
Martin Weitzman, Harvard University, delivers a lecture entitled, "Climate Sensitivity and Some Speculative Implications for Modeling Catastrophes", at the YCEI conference, "Uncertainty in Climate Change: A Conversation with Climate Scientists and Economists".
From playlist Uncertainty in Climate Change: A Conversation with Climate Scientists and Economists