From playlist Coursera Regression V2
We propose a sparse regression method capable of discovering the governing partial differential equation(s) of a given system by time series measurements in the spatial domain. The regression framework relies on sparsity promoting techniques to select the nonlinear and partial derivative
From playlist Research Abstracts from Brunton Lab
(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 9.2) Linear regression - Definition & Motivation
Linear regression arises naturally from a sequence of simple choices: discriminative model, Gaussian distributions, and linear functions. A playlist of these Machine Learning videos is available here: http://www.youtube.com/view_play_list?p=D0F06AA0D2E8FFBA
From playlist Machine Learning
(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
10g Machine Learning: Isotonic Regression
Lecture on isotonic regression. Introduces the idea of a piece-wise linear model with monotonic constraint. Follow along with the demonstration workflow: https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/SubsurfaceDataAnalytics_IsotonicRegression.ipynb
From playlist Machine Learning
Jamie Haddock - Hierarchical and neural nonnegative tensor factorizations - IPAM at UCLA
Recorded 02 December 2022. Jamie Haddock of Harvey Mudd College presents "Hierarchical and neural nonnegative tensor factorizations" at IPAM's Multi-Modal Imaging with Deep Learning and Modeling Workshop. Abstract: Nonnegative matrix factorization (NMF) has found many applications includin
From playlist 2022 Multi-Modal Imaging with Deep Learning and Modeling
Intro to Linear Systems: 2 Equations, 2 Unknowns - Dr Chris Tisdell Live Stream
Free ebook http://tinyurl.com/EngMathYT Basic introduction to linear systems. We discuss the case with 2 equations and 2 unknowns. A linear system is a mathematical model of a system based on the use of a linear operator. Linear systems typically exhibit features and properties that ar
From playlist Intro to Linear Systems
Kaggle Reading Group: Dissecting contextual word embeddings (Part 4) | 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 unbalance: EDA,PCA,SMOTE,LR,SVM,DT,RF" by
From playlist Kaggle Reading Group | Kaggle
Neural networks and the brain: from the retina to semantic cognition - Surya Ganguli
Surya Ganguli research spans the fields of neuroscience, machine learning and physics, focusing on understanding and improving how both biological and artificial neural networks learn striking emergent computations. In this talk Dr. Ganguli shows how a synthesis of machine learning, neuros
From playlist Wu Tsai Neurosciences Institute
Workshop on Theory of Deep Learning: Where next? Topic: Spotlight Talks Speakers: Yuanzhi Li, Soham De, Mahyar Fazlyab, Maithra Raghu, Valentin Thomas Date: October 15, 2019 For more video please visit http://video.ias.edu
From playlist Mathematics
Seminar 9: Surya Ganguli - Statistical Physics of Deep Learning
MIT RES.9-003 Brains, Minds and Machines Summer Course, Summer 2015 View the complete course: https://ocw.mit.edu/RES-9-003SU15 Instructor: Surya Ganguli Describes how the application of methods from statistical physics to the analysis of high-dimensional data can provide theoretical insi
From playlist MIT RES.9-003 Brains, Minds and Machines Summer Course, Summer 2015
On Expressiveness and Optimization in Deep Learning - Nadav Cohen
Members' Seminar Topic: On Expressiveness and Optimization in Deep Learning Speaker: Nadav Cohen Affiliation: Member, School of Mathematics Date: April 2, 2018 For more videos, please visit http://video.ias.edu
From playlist Mathematics
AIUK 2022 WORKSHOP - Fleet Modelling: Digital twins
Research in Action at AI UK 2022 was a series of interactive workshops designed to connect researchers with external stakeholders to solve real-world problems.
From playlist AIUK 2022 Workshops
Multi-Output Prediction: Theory and Practice - Inderjit Dhillon
Seminar on Theoretical Machine Learning Topic: Multi-Output Prediction: Theory and Practice Speaker: Inderjit Dhillon Affiliation: University of Texas, Austin Date: August 27, 2020 For more video please visit http://video.ias.edu
From playlist Mathematics
Predictive Modelling Techniques | Data Science With R Tutorial
🔥 Advanced Certificate Program In Data Science: https://www.simplilearn.com/pgp-data-science-certification-bootcamp-program?utm_campaign=PredictiveModeling-0gf5iLTbiQM&utm_medium=Descriptionff&utm_source=youtube 🔥 Data Science Bootcamp (US Only): https://www.simplilearn.com/data-science-bo
From playlist R Programming For Beginners [2022 Updated]
Deep Learning of Hierarchical Multiscale Differential Equation Time Steppers
This video by Yuying Liu introduces a new deep learning architecture to accurately and efficiently integrate multiscale differential equations forward in time. This approach is benchmarked on several illustrative dynamical systems. Check out the paper on arXiv: https://arxiv.org/abs/20
From playlist Data-Driven Science and Engineering
Introduces notation and formulas for exponential growth models, with solutions to guided problems.
From playlist Discrete Math