(ML 13.7) Graphical model for Bayesian Naive Bayes
As an example, we write down the graphical model for Bayesian naïve Bayes.
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
Formal Definition of a Function using the Cartesian Product
Learning Objectives: In this video we give a formal definition of a function, one of the most foundation concepts in mathematics. We build this definition out of set theory. **************************************************** YOUR TURN! Learning math requires more than just watching vid
From playlist Discrete Math (Full Course: Sets, Logic, Proofs, Probability, Graph Theory, etc)
Introduction to Classification Models
Ever wonder what classification models do? In this quick introduction, we talk about what classifications models are, as well as what they are used for in machine learning. In machine learning there are many different types of models, all with different types of outcomes. When it comes t
From playlist Introduction to Machine Learning
(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
(ML 13.2) Directed graphical models - introductory examples (part 2)
Introduction to (directed) graphical models. Simple examples to motivate the concept.
From playlist Machine Learning
Alison Etheridge: Spatial population models (4/4)
Abstract: Mathematical models play a fundamental role in theoretical population genetics and, in turn, population genetics provides a wealth of mathematical challenges. In these lectures, we focus on some of the models which arise when we try to model the interplay between the forces of ev
From playlist Summer School on Stochastic modelling in the life sciences
Pierre-Yves Oudeyer - Developmental AI: machines that learn like children
Current approaches to Al and machine learning are still fundamentally limited in comparison with the amazing learning capabilities of children. What is remarkable is not that some children become world champions in certain games or specialties: it is rather their autonomy, open-endedness,
From playlist LSC 2022
Re-Imagining the Social Sciences in the Age of AI - March 4, 2020
Re-Imagining the Social Sciences in the Age of AI: A Cross-Disciplinary Conversation Wednesday, March 4 5:30 p.m. Wolfensohn Hall Co-organized by the School of Mathematics and the School of Social Sciences, this public event will feature two short talks about the transformational possibi
From playlist Mathematics
Interdisciplinarity in the Age of Networks - Jennifer Chayes
Jennifer Chayes Managing Director, Microsoft Research New England, Microsoft Research New York May 21, 2013 For more videos, visit http://video.ias.edu
From playlist Mathematics
Oxford Mathematics Public Lectures: Sanjeev Goyal - The Law of the Few The study of networks offers a fruitful approach to understanding human behaviour. Sanjeev Goyal is one of its pioneers. In this lecture Sanjeev presents a puzzle: In social communities, the vast majority of individua
From playlist Oxford Mathematics Public Lectures
(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
Revisión del libro: Grokking Machine Learning - Luis Serrano
Book Movement es una comunidad que busca cambiar la vida de las personas dejando un legado de experiencias y conocimiento. Revisamos libros de Ciencia y Negocios todos los Martes y Jueves respectivamente. INVITADO Luis Serrano Científico Investigador de Inteligencia Artificial Cuántica e
From playlist Charlas y entrevistas en Español
Alison Etheridge: Spatial population models (2/4)
Abstract: Mathematical models play a fundamental role in theoretical population genetics and, in turn, population genetics provides a wealth of mathematical challenges. In these lectures, we focus on some of the models which arise when we try to model the interplay between the forces of ev
From playlist Summer School on Stochastic modelling in the life sciences
Panel Discussion: Challenges and Opportunities
Ideas 2017–18 closed with a panel discussion moderated by Dijkgraaf, Institute Director and Leon Levy Professor, with Nicola Di Cosmo, Luce Foundation Professor in East Asian Studies in the School of Historical Studies, Johan Heilbron, Louise and John Steffens Founders' Circle Member in th
From playlist Ideas 2017-18
CS224W: Machine Learning with Graphs | 2021 | Lecture 16.4 - Robustness of Graph Neural Networks
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3Csn5T7 Jure Leskovec Computer Science, PhD For the last segment of our discussion on advanced GNN topics, we discuss the robustness of GNNs. We first introduce th
From playlist Stanford CS224W: Machine Learning with Graphs
Stanford Seminar - Computational Epidemiology: The Role of Big Data and Pervasive Informatics
"Computational Epidemiology: The role of big data and pervasive informatics" - Madhav Marathe of Virginia Tech Colloquium on Computer Systems Seminar Series (EE380) presents the current research in design, implementation, analysis, and use of computer systems. Topics range from integrated
From playlist Engineering
Alison Etheridge: Spatial population models (3/4)
Abstract: Mathematical models play a fundamental role in theoretical population genetics and, in turn, population genetics provides a wealth of mathematical challenges. In these lectures, we focus on some of the models which arise when we try to model the interplay between the forces of ev
From playlist Summer School on Stochastic modelling in the life sciences
How Learning Ten Equations Can Improve Your Life - David Sumpter
Mathematics has a lot going for it, but David Sumpter argues that it can not only provide you with endless YouTube recommendations, and even make you rich, but it can make you a better person. Our latest Oxford Mathematics Public Lecture. Oxford Mathematics Public Lectures are generousl
From playlist Oxford Mathematics Public Lectures