Causal Inference is a set of tools used to scientifically prove cause and effect, very commonly used in economics and medicine. This series will go over the basics that any data scientist should understand about causal inference - and point them to the tools they would need to perform it.
From playlist Causal Inference - The Science of Cause and Effect
Ideal Experiment - Causal Inference
In this video, I give you more details about the fundamental question and the fundamental problem of causal inference with the help of an example (our ideal experiment).
From playlist Causal Inference - The Science of Cause and Effect
Discrete Math - 1.6.1 Rules of Inference for Propositional Logic
Building a valid argument using rules of inference for propositions. Textbook: Rosen, Discrete Mathematics and Its Applications, 7e Playlist: https://www.youtube.com/playlist?list=PLl-gb0E4MII28GykmtuBXNUNoej-vY5Rz
From playlist Discrete Math I (Entire Course)
RULES of INFERENCE - DISCRETE MATHEMATICS
We talk about rules of inference and what makes a valid argument. We discuss modus ponens, modus tollens, hypothetical syllogism, disjunctive syllogism, addition, simplification, and conjunction. #DiscreteMath #Mathematics #Logic #RulesOfInference LIKE AND SHARE THE VIDEO IF IT HELPED!
From playlist Discrete Math 1
t Test Write Up of a Hypothesis Test of an Unknown Population Mean
How to perform and write up a hypothesis test [t test] of an unknown population mean [In accordance with AP Statistics requirements]
From playlist Unit 9: t Inference and 2-Sample Inference
Assumptions - Causal Inference
In this video, I introduce the most important assumptions in casual inference that we use in order to avoid mistakes such as presuming association and causation to be one and the same, among others: - Positivity - SUTVA - Large Sample Size - Double Blinded - No Measurement Error - Exchan
From playlist Causal Inference - The Science of Cause and Effect
Fundamental Question - Causal Inference
In this video, I define the fundamental question and problem of causal inference and use an example to further explain the concept.
From playlist Causal Inference - The Science of Cause and Effect
2 Sample t Test v Paired t Test
Identifying the difference between situations when a 2-sample t Test is appropriate and when a paired t Test is appropriate, including the recognition of paired dependent data versus independent samples.
From playlist Unit 9: t Inference and 2-Sample Inference
Logic 4 - Inference Rules | Stanford CS221: AI (Autumn 2021)
For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai Associate Professor Percy Liang Associate Professor of Computer Science and Statistics (courtesy) https://profiles.stanford.edu/percy-liang Assistant Professor
From playlist Stanford CS221: Artificial Intelligence: Principles and Techniques | Autumn 2021
Brief Introduction to Statistical Inference - Causal Inference
In this video, I briefly introduce the topic of Statistical Inference and go over its most fundamental concepts - those that we will use in this series. If you want to learn more about this stuff, check out this link to my entire series on Statistical Inference: https://www.youtube.com/pla
From playlist Causal Inference - The Science of Cause and Effect
Billy Price and Will Troiani present a series of seminars on foundations of mathematics. In this seminar Billy introduces natural deduction as a proof system. You can join this seminar from anywhere, on any device, at https://www.metauni.org. This video was filmed in Deprecation (https:/
From playlist Foundations seminar
How to create a fuzzy inference system
Learn how to graphically design and simulate fuzzy inference systems using the fuzzy logic designer app. The video demonstrates the steps to create a fuzzy logic to estimate the tip percentage for a waiter based on the quality of food and service. - Build fuzzy inference systems and fuzz
From playlist “How To” with MATLAB and Simulink
Logic 1 - Propositional Logic | Stanford CS221: AI (Autumn 2019)
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3ChWesU Topics: Logic Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor - Stanford University http://onlinehub.stanford.edu/ Associate Professor
From playlist Stanford CS221: Artificial Intelligence: Principles and Techniques | Autumn 2019
This video functions as a brief introduction to many different topics in formal logic. Notes on the Images: I looked into the legality of using images for this video a good deal and I've come to the conclusion that there is nothing in this video which could remotely imply these images ar
From playlist Summer of Math Exposition 2 videos
Logic 1 - Overview: Logic Based Models | Stanford CS221: AI (Autumn 2021)
For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai This lecture covers logic-based models: propositional logic, first order logic Applications: theorem proving, verification, reasoning, think in terms of logical f
From playlist Stanford CS221: Artificial Intelligence: Principles and Techniques | Autumn 2021
Logic 10 - Recap | Stanford CS221: Artificial Intelligence (Autumn 2021)
For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/ai Associate Professor Percy Liang Associate Professor of Computer Science and Statistics (courtesy) https://profiles.stanford.edu/percy-liang Assistant Professor
From playlist Stanford CS221: Artificial Intelligence: Principles and Techniques | Autumn 2021
Geoffrey Hinton: "Introduction to Deep Learning & Deep Belief Nets"
Graduate Summer School 2012: Deep Learning, Feature Learning "Part 1: Introduction to Deep Learning & Deep Belief Nets" Geoffrey Hinton, University of Toronto Institute for Pure and Applied Mathematics, UCLA July 9, 2012 For more information: https://www.ipam.ucla.edu/programs/summer-sc
From playlist GSS2012: Deep Learning, Feature Learning
Inference Rules via the Algebra of Boole | MathFoundations 275 | N J Wildberger
We show how to introduce Inference Rules in Propositional Logic in the framework of the Algebra of Boole, which provides a cut and dried technology to easily establish all such rules. Prominent amongst these are Modus Ponens, Modus Tollens, Hypothetical Syllogism and Disjunctive Syllogism
From playlist Boole's Logic and Circuit Analysis