Probabilistic logic programming and its applications - Luc De Raedt, Leuven
Probabilistic programs combine the power of programming languages with that of probabilistic graphical models. There has been a lot of progress in this paradigm over the past twenty years. This talk will introduce probabilistic logic programming languages, which are based on Sato's distrib
From playlist Logic and learning workshop
Probabilistic Python: An Introduction to Bayesian Modeling with PyM || Chris Fonnesbeck
Bayesian statistical methods offer a powerful set of tools to tackle a wide variety of data science problems. In addition, the Bayesian approach generates results that are easy to interpret and automatically account for uncertainty in quantities that we wish to estimate and predict. Histor
From playlist Python
Injective, Surjective and Bijective Functions (continued)
This video is the second part of an introduction to the basic concepts of functions. It looks at the different ways of representing injective, surjective and bijective functions. Along the way I describe a neat way to arrive at the graphical representation of a function.
From playlist Foundational Math
Definition of a Surjective Function and a Function that is NOT Surjective
We define what it means for a function to be surjective and explain the intuition behind the definition. We then do an example where we show a function is not surjective. Surjective functions are also called onto functions. Useful Math Supplies https://amzn.to/3Y5TGcv My Recording Gear ht
From playlist Injective, Surjective, and Bijective Functions
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)
Learning to simplify a rational expression
Learn how to simplify rational expressions. A rational expression is an expression in the form of a fraction where the numerator and/or the denominator are/is an algebraic expression. To simplify a rational expression, we factor completely the numerator and the denominator of the rational
From playlist Simplify Rational Expressions
Abstract Algebra | Surjective Functions
We give the definition of a surjective function, an outline for proving that a function is surjective, and some examples. http://www.michael-penn.net http://www.randolphcollege.edu/mathematics/
From playlist Abstract Algebra
Functional Programming for Beginners: Pure Functions Explained
Why is it that pure functions can’t use mutable states in most cases, and how do you fight mutability by working with immutable values? Check it out in this live coding session by Michal Plachta, the author of Grokking Functional Programming. Watch the full video at: http://mng.bz/J24a 📚
From playlist Functional Programming
Introduction to Predicate Logic
This video introduces predicate logic. mathispower4u.com
From playlist Symbolic Logic and Proofs (Discrete Math)
Atılım Güneş Baydin: "Universal Probabilistic Programming in Simulators"
Machine Learning for Physics and the Physics of Learning 2019 Workshop II: Interpretable Learning in Physical Sciences "Universal Probabilistic Programming in Simulators" Atılım Güneş Baydin, University of Oxford Abstract: We present a novel probabilistic programming framework that coupl
From playlist Machine Learning for Physics and the Physics of Learning 2019
Semantic models for higher-order Bayesian inference - Sam Staton, University of Oxford
In this talk I will discuss probabilistic programming as a method of Bayesian modelling and inference, with a focus on fully featured probabilistic programming languages with higher order functions, soft constraints, and continuous distributions. These languages are pushing the limits of e
From playlist Logic and learning workshop
Bayesian Networks 1 - Inference | Stanford CS221: AI (Autumn 2019)
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3bcQMeG Topics: Bayesian Networks Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor - Stanford University http://onlinehub.stanford.edu/ Associa
From playlist Stanford CS221: Artificial Intelligence: Principles and Techniques | Autumn 2019
Bayesian Networks 3 - Probabilistic Programming | 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
Probability theory and AI | The Royal Society
Join Professor Zoubin Ghahramani to explore the foundations of probabilistic AI and how it relates to deep learning. 🔔Subscribe to our channel for exciting science videos and live events, many hosted by Brian Cox, our Professor for Public Engagement: https://bit.ly/3fQIFXB #Probability #A
From playlist Latest talks and lectures
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
23. Probabilistic Computation, BPP
MIT 18.404J Theory of Computation, Fall 2020 Instructor: Michael Sipser View the complete course: https://ocw.mit.edu/18-404JF20 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP60_JNv2MmK3wkOt9syvfQWY Quickly reviewed last lecture. Defined probabilistic Turing machines
From playlist MIT 18.404J Theory of Computation, Fall 2020
Bayesian Inference by Program Verification - Joost-Pieter Katoen, RWTH Aachen University
In this talk, I will give a perspective on inference in Bayes' networks (BNs) using program verification. I will argue how weakest precondition reasoning a la Dijkstra can be used for exact inference (and more). As exact inference is NP-complete, inference is typically done by means of sim
From playlist Logic and learning workshop
Josh Tenenbaum - Cognitive and computational foundations for collective human intelligence
Recorded 15 February 2022. Josh Tenenbaum of the Massachusetts Institute of Technology presents "Cognitive and computational foundations for collective human intelligence" at IPAM's Mathematics of Collective Intelligence Workshop. Learn more online at: http://www.ipam.ucla.edu/programs/wor
From playlist Workshop: Mathematics of Collective Intelligence - Feb. 15 - 19, 2022.