R - Hierarchical Models Examples
Lecturer: Dr. Erin M. Buchanan Missouri State University Summer 2016 This example video covers how to perform a first order CFA, second order CFA, and bi-factor CFA. Lavaan, semPath, and the cfa functions are covered, along with interpretation of the models and some guidance on how to pi
From playlist Structural Equation Modeling
R - Hierarchical Confirmatory Factor Analysis Lecture
Lecturer: Dr. Erin M. Buchanan Missouri State University Summer 2016 This lecture covers the basics to understanding a hierarchical CFA, in contrast to a bifactor CFA model. Interpretation and discussion of the theoretical differences between these models and first order models are discu
From playlist Structural Equation Modeling
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 12.1) Model selection - introduction and examples
Introduction to the basic idea of model selection, and some examples: linear regression using MLE, Bayesian linear regression, and k-nearest neighbor.
From playlist Machine Learning
From playlist Hierarchical Clustering
Data structures: Introduction to Trees
See complete series on data structures here: http://www.youtube.com/playlist?list=PL2_aWCzGMAwI3W_JlcBbtYTwiQSsOTa6P In this lesson, we have described tree data structure as a logical model in computer science. We have briefly discussed tree as a non-linear hierarchical data structure, i
From playlist Data structures
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
Mathematical modeling of evolving systems
Discover the multidisciplinary nature of the dynamical principles at the core of complexity science. COURSE NUMBER: CAS 522 COURSE TITLE: Dynamical Systems LEVEL: Graduate SCHOOL: School of Complex Adaptive Systems INSTRUCTOR: Enrico Borriello MODE: Online SEMESTER: Fall 2021 SESSION:
From playlist What is complex systems science?
Clusterfck A Practical Guide to Bayesian Hierarchical Modeling in PyMC3 || Hanna van der Vlis
At Apollo Agriculture, a Kenya based agro-tech startup, one of the challenging problems we face is to predict yields of Kenyan maize farmers. Like almost all data-sets, this data-set has a hierarchical structure: farmers within the same region arenāt independent. By ignoring this fact, a m
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
Ruslan Salakhutdinov: "Advanced Hierarchical Models"
Graduate Summer School 2012: Deep Learning, Feature Learning "Advanced Hierarchical Models" Ruslan Salakhutdinov Institute for Pure and Applied Mathematics, UCLA July 24, 2012 For more information: https://www.ipam.ucla.edu/programs/summer-schools/graduate-summer-school-deep-learning-fe
From playlist GSS2012: Deep Learning, Feature Learning
Building a Suite of Learning Analytics Tools for STEM Education
This is a demonstration of some of the early development in learning analytics tools for STEM education. The demonstration describes early results from these tools' deployment in a real classroom and the opportunities that exist.
From playlist Wolfram Technology Conference 2021
DIRECT 2021 10 Deep Learning Multi-scale Modeling
DIRECT Consortium at The University of Texas at Austin, working on novel methods and workflows in spatial, subsurface data analytics, geostatistics and machine learning. This is Deep Learning for Multi-scale Modeling by Wen Pan with Prof. Carlos Torres-Verdin. Join the consortium for ac
From playlist DIRECT Consortium, The University of Texas at Austin
Unsupervised Learning | Unsupervised Learning Algorithms | Machine Learning Tutorial | Simplilearn
š„Artificial Intelligence Engineer Program (Discount Coupon: YTBE15): https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=UnsupervisedLearning-D6gtZrsYi6c&utm_medium=Descriptionff&utm_source=youtube š„Professional Certificate Program In AI And Machine Learning: https:
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
[hgraph2graph] Hierarchical Generation of Molecular Graphs using Structural Motifs | AISC Spotlight
For slides and more information on the paper, visit https://aisc.ai.science/events/2020-02-26 Discussion lead: Wengong Jin Discussion facilitator(s): Rouzbeh Afrasiabi
From playlist Machine Learning for Scientific Discovery
Data Structures: List as abstract data type
See complete series of videos in data structures here: http://www.youtube.com/playlist?list=PL2_aWCzGMAwI3W_JlcBbtYTwiQSsOTa6P&feature=view_all In this lesson, we will introduce a dynamic list structure as an abstract data type and then see one possible implementation of dynamic list using
From playlist Data structures
CS224W: Machine Learning with Graphs | 2021 | Lecture 19.2 - Hyperbolic Graph Embeddings
For more information about Stanfordās Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3Brc7vN Jure Leskovec Computer Science, PhD In previous lectures, we focused on graph representation learning in Euclidean embedding spaces. In this lecture, we in
From playlist Stanford CS224W: Machine Learning with Graphs