How to Model Custom Physical Components in Simscape
Simscape™ extends the MATLAB® language with constructs for modeling implicit equations. Learn more about Simscape: http://goo.gl/Jhsth7 Get a free Product Trial: https://goo.gl/5NvCdU Download Sample Lift Table Model: http://goo.gl/k4fYwA These extensions of MATLAB are used to model a tra
From playlist Physical Modeling
(ML 13.3) Directed graphical models - formalism (part 1)
Definition of a directed graphical model, or more precisely, what it means for a distribution to respect a directed acyclic graph.
From playlist Machine Learning
(ML 13.4) Directed graphical models - formalism (part 2)
Definition of a directed graphical model, or more precisely, what it means for a distribution to respect a directed acyclic graph.
From playlist Machine Learning
(ML 13.6) Graphical model for Bayesian linear regression
As an example, we write down the graphical model for Bayesian linear regression. We introduce the "plate notation", and the convention of shading random variables which are being conditioned on.
From playlist Machine Learning
Learn the basics of how to code models in R as part of the boot camp for the Summer Institutes in Computational Social Science (with Duke Professor Chris Bail).
From playlist All Videos
Data Modeling Tutorial | Data Modeling for Data Warehousing | Data Warehousing Tutorial | Edureka
***** Data Warehousing & BI Training: https://www.edureka.co/data-warehousing-and-bi ***** Data modeling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations. Therefore, th
From playlist Data Warehousing Tutorial Videos
How to Model Run-Time Software with Simulink
Real-Time Software Modeling helps you implement large-scale real-time systems by defining the components in a composition; simulating and testing them; generating code; and integrating the code into a larger system. This video series covers different modeling styles in Simulink® for partit
From playlist Run-Time Software Modeling
(ML 13.1) Directed graphical models - introductory examples (part 1)
Introduction to (directed) graphical models. Simple examples to motivate the concept.
From playlist Machine Learning
If you are interested in learning more about this topic, please visit http://www.gcflearnfree.org/ to view the entire tutorial on our website. It includes instructional text, informational graphics, examples, and even interactives for you to practice and apply what you've learned.
From playlist 3D Printing
Lecture 5A: Assignment, State, and Side-effects
MIT 6.001 Structure and Interpretation of Computer Programs, Spring 2005 Instructor: Harold Abelson, Gerald Jay Sussman, Julie Sussman View the complete course: https://ocw.mit.edu/6-001S05 YouTube Playlist: https://www.youtube.com/playlist?list=PLE18841CABEA24090 Assignment, State, and S
From playlist MIT 6.001 Structure and Interpretation, 1986
Tutorial 5.1: Tomer Ullman - Church Programming Language Part 1
MIT RES.9-003 Brains, Minds and Machines Summer Course, Summer 2015 View the complete course: https://ocw.mit.edu/RES-9-003SU15 Instructor: Tomer Ullman Learn the Church programming language, which facilitates the implementation and testing of generative models of physical and social worl
From playlist MIT RES.9-003 Brains, Minds and Machines Summer Course, Summer 2015
Comparing machine learning models in scikit-learn
We've learned how to train different machine learning models and make predictions, but how do we actually choose which model is "best"? We'll cover the train/test split process for model evaluation, which allows you to avoid "overfitting" by estimating how well a model is likely to perform
From playlist Machine learning in Python with scikit-learn
ResNet Strikes Back! | Patches Are All You Need? | Papers Explained
❤️ Become The AI Epiphany Patreon ❤️ ► https://www.patreon.com/theaiepiphany 👨👩👧👦 JOIN OUR DISCORD COMMUNITY: Discord ► https://discord.gg/peBrCpheKE 📢 SUBSCRIBE TO MY MONTHLY AI NEWSLETTER: Substack ► https://aiepiphany.substack.com/ In this video I cover 2 recent papers: * ResNet
From playlist Computer Vision
Lecture 5A | MIT 6.001 Structure and Interpretation, 1986
Assignment, State, and Side-effects Despite the copyright notice on the screen, this course is now offered under a Creative Commons license: BY-NC-SA. Details at http://ocw.mit.edu/terms Subtitles for this course are provided through the generous assistance of Henry Baker, Hoofar Pou
From playlist MIT 6.001 Structure and Interpretation, 1986
Procedural Programming: It's Back? It Never Went Away
When programmers describe code as 'procedural', it's generally not meant as a compliment. There is the belief that we have collectively moved pass such thinking and onto better paradigms. But a paradigm is no more than a pattern language, a family of solutions fit for a context. Change the
From playlist Software Development
Statistical Rethinking 2022 Lecture 01 - Golem of Prague
Introduction to the course: Goals, golems, drawing the owl, meet a DAG Slides available on course webpage: https://github.com/rmcelreath/stat_rethinking_2022 Intro music: https://www.youtube.com/watch?v=S61ENc51Z1Q Prague music: https://www.youtube.com/watch?v=vO6x49wBKi8 Chapters: 00:00
From playlist Statistical Rethinking 2022
Gaël Varoquaux - Supervised Learning with Missing Values
Some data come with missing values. For instance, a survey’s participant may ignore some questions. There is an abundant statistical literature on this topic, establishing for instance how to fit model without biases due to the missingness, and imputation strategies to provide practical so
From playlist Journée statistique & informatique pour la science des données à Paris-Saclay 2021
Elisabeth Gassiat: Bayesian multiple testting for dependent data and hidden Markov... - lecture 1
HYBRID EVENT Recorded during the meeting "End-to-end Bayesian Learning Methods " the October 26, 2021 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide mathematicians on CIRM's
From playlist Probability and Statistics
Tony Lelievre (DDMCS@Turing): Coarse-graining stochastic dynamics
Complex models in all areas of science and engineering, and in the social sciences, must be reduced to a relatively small number of variables for practical computation and accurate prediction. In general, it is difficult to identify and parameterize the crucial features that must be incorp
From playlist Data driven modelling of complex systems