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
Decision Trees are more powerful than you think
Let's talk about how decision trees can be used for modeling and causal inference! Follow me on M E D I U M: https://towardsdatascience.com/likelihood-probability-and-the-math-you-should-know-9bf66db5241b Joins us on D I S C O R D: https://discord.gg/3C6fKZ3E5m Please like and S U B S C R
From playlist Causal Inference
(ML 7.1) Bayesian inference - A simple example
Illustration of the main idea of Bayesian inference, in the simple case of a univariate Gaussian with a Gaussian prior on the mean (and known variances).
From playlist Machine Learning
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
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
Statistical Inference for Causal Inference - Causal Inference
In this video I explain the concept of statistical inference for causal inference through a realistic group ideal experiment example. Enjoy! Here's the link to my previous Statistical Inference Introduction video if you haven't watched it yet: https://youtu.be/fEGc8ZqveXM
From playlist Causal Inference - The Science of Cause and Effect
Group Ideal Experiment - Causal Inference
In this video, I explain the concept of a group ideal experiment wherein I introduce some more causal inference terminology! I also go over the fundamental problem of causal inference and the problem of statistical inference. Enjoy!
From playlist Causal Inference - The Science of Cause and Effect
Free Will - General Philosophy (Peter Millican)
In this lecture on General Philosophy, Professor Peter Millican discusses free will and the problem of moral responsibility. This comes from a 2010 series on General Philosophy. The slides have been added. This series of lectures was delivered by Peter Millican to first-year philosophy st
From playlist Free Will, Determinism, & Action
Predicting and Understanding Human Choices using PCMC-Net with an application to Airline Itineraries
Speaker(s): Alix Lheritier Facilitator(s): Omar Nada Find the recording, slides, and more info at https://ai.science/e/predicting-and-understanding-human-choices-using-pcmc-net-with-an-application-to-airline-itineraries--T7VHeDI6OAv0cXM7HWYT Motivation / Abstract The work focuses on pred
From playlist Recommender Systems
Type Systems I - Vladimir Voevodsky
Vladimir Voevodsky Institute for Advanced Study November 28, 2012 For more videos, visit http://video.ias.edu
From playlist Mathematics
Kaggle Reading Group: Dissecting contextual word embeddings (Part 2) | Kaggle
Join Kaggle Data Scientist Rachael as she reads through an NLP paper! Today's paper is "Dissecting contextual word embeddings: Architecture and representation" (Peters et al, 2018). You can find a copy here: https://aclweb.org/anthology/D18-1179 SUBSCRIBE: https://www.youtube.com/c/kaggle?
From playlist Kaggle Reading Group | Kaggle
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
Examples of Selection Bias - Causal Inference
Today I talk about several distinct examples of selection bias.
From playlist Causal Inference - The Science of Cause and Effect
Bayesian optimisation for likelihood-free cosmological (...) - Leclercq - Workshop 2 - CEB T3 2018
Leclercq (Imperial College) / 22.10.2018 Bayesian optimisation for likelihood-free cosmological inference ---------------------------------- Vous pouvez nous rejoindre sur les réseaux sociaux pour suivre nos actualités. Facebook : https://www.facebook.com/InstitutHenriPoincare/ Twitter
From playlist 2018 - T3 - Analytics, Inference, and Computation in Cosmology
Stanford CS330: Deep Multi-task and Meta Learning | 2020 | Lecture 13: A Graphical Model Perspective
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai A Graphical Model Perspective on Multi-Task and Meta-RL To follow along with the course, visit: https://cs330.stanford.edu/ To view all online courses and pro
From playlist Stanford CS330: Deep Multi-task and Meta Learning | Autumn 2020
Type Systems II- Vladimir Voevodsky
Vladimir Voevodsky Professor, Institute for Advanced Study December 5, 2012 For more videos, visit http://video.ias.edu
From playlist Mathematics
Parallel Processing in Python || Aaron Richter
Python has a vast ecosystem of tools for scientific computing and data science. However, when data size or computational complexity grows, users may encounter performance challenges. This talk will cover the current landscape of parallel processing tools in Python, with a focus on which to
From playlist Python
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
Dr Natalia Bochkina, Edinburgh University
Natalia Bochkina joined the University of Edinburgh as a Lecturer in Statistics in 2007. In 2003-2007 she was a postdoc at the Biostatistics group at the Imperial College London working on the collaborative project building a biological atlas of insulin resistance. In 2002-2003 she was a s
From playlist Short Talks