Expert systems | Automated reasoning
In artificial intelligence, model-based reasoning refers to an inference method used in expert systems based on a model of the physical world. With this approach, the main focus of application development is developing the model. Then at run time, an "engine" combines this model knowledge with observed data to derive conclusions such as a diagnosis or a prediction. (Wikipedia).
Sebastian Reich (DDMCS@Turing): Learning models by making them interact
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
(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
(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
(ML 11.8) Bayesian decision theory
Choosing an optimal decision rule under a Bayesian model. An informal discussion of Bayes rules, generalized Bayes rules, and the complete class theorems.
From playlist Machine Learning
Jonathan Weare (DDMCS@Turing): Stratification for Markov Chain Monte Carlo
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
Eric Vanden-Eijnden (DDMCS@Turing): Neural networks as interacting particle systems
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
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
(ML 12.4) Bayesian model selection
Approaches to model selection from a Bayesian perspective: Bayesian model averaging (BMA), "Type II MAP", and Type II Maximum Likelihood (a.k.a. ML-II, a.k.a. the evidence approximation, a.k.a. empirical Bayes).
From playlist Machine Learning
Model Theory - part 03 - Terms, Formulas, Sequents
He we are a little bit more precise about keeping track of what fragments of formal languages we are using. This becomes relevant when you want to interpret them later. Caramello's book was useful in preparing this. We also found the post on nCatLab useful.
From playlist Model Theory
Why not to be afraid of priors (too much), Paul-Christian Bürkner - Bayes@Lund 2018
More info about Bayes@Lund, including slides: https://bayesat.github.io/lund2018/bayes_at_lund_2018.html
From playlist Bayes@Lund 2018
André Freitas - Building explanation machines for science: a neuro-symbolic perspective
Recorded 12 January 2023. André Freitas of the University of Manchester presents "Building explanation machines for science: a neuro-symbolic perspective" at IPAM's Explainable AI for the Sciences: Towards Novel Insights Workshop. Learn more online at: http://www.ipam.ucla.edu/programs/wor
From playlist 2023 Explainable AI for the Sciences: Towards Novel Insights
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
Exploring foundation models - Session 2
Speakers: Katie Collins PhD Student, Machine Learning Group Professor Mirella Lapata Professor in the School of Informatics, University of Edinburgh 00:00 Human-Centric Benchmarking 25:00 How did you go about recruiting the ‘right humans’ you would include in this benchmarking process?
From playlist Exploring Foundation Models
Pascal Fontaine - SMT: quantifiers, and future prospects - IPAM at UCLA
Recorded 16 February 2023. Pascal Fontaine of the Université de Liège presents "SMT: quantifiers, and future prospects" at IPAM's Machine Assisted Proofs Workshop. Abstract: Satisfiability Modulo Theory (SMT) is a paradigm of automated reasoning to tackle problems related to formulas conta
From playlist 2023 Machine Assisted Proofs Workshop
Introduction to NetLogo by Bill Rand
Program Summer Research Program on Dynamics of Complex Systems ORGANIZERS: Amit Apte, Soumitro Banerjee, Pranay Goel, Partha Guha, Neelima Gupte, Govindan Rangarajan and Somdatta Sinha DATE : 15 May 2019 to 12 July 2019 VENUE : Madhava hall for Summer School & Ramanujan hall f
From playlist Summer Research Program On Dynamics Of Complex Systems 2019
Isabelle Bloch - Hybrid AI for Knowledge Representation and Model-based Image Understanding - (...)
This presentation will focus on hybrid AI, as a step towards explainability, more specifically in the domain of spatial reasoning and image understanding. Image understanding benefits from the modeling of knowledge about both the scene observed and the objects it contains as well as their
From playlist 8th edition of the Statistics & Computer Science Day for Data Science in Paris-Saclay, 9 March 2023
CMU Neural Nets for NLP 2017 (22): Machine Reading w/ Neural Nets
This lecture (by Graham Neubig) for CMU CS 11-747, Neural Networks for NLP (Fall 2017) covers: * Machine Reading * Attention-based Machine Reading Models * Multi-hop Reasoning Models Slides: http://phontron.com/class/nn4nlp2017/assets/slides/nn4nlp-22-machinereading.pdf Previous Video:
From playlist CMU Neural Nets for NLP 2017
20 Data Analytics: Decision Tree
Lecture on decision tree-based machine learning with workflows in R and Python and linkages to bagging, boosting and random forest.
From playlist Data Analytics and Geostatistics
Stanford Seminar - Bridging model-based and data-driven reasoning for safe human-centered robotics
Jaime Fisac is an Assistant Professor of Electrical and Computer Engineering at Princeton University. This talk was given on September 27, 2019. Spurred by recent advances in perception and decision-making, robotic technologies are undergoing a historic expansion from factory floors to t
From playlist Stanford AA289 - Robotics and Autonomous Systems Seminar