(ML 11.4) Choosing a decision rule - Bayesian and frequentist
Choosing a decision rule, from Bayesian and frequentist perspectives. To make the problem well-defined from the frequentist perspective, some additional guiding principle is introduced such as unbiasedness, minimax, or invariance.
From playlist Machine Learning
Causal Behavioral Modeling Framework - Discrete Choice Modeling of Consumer Demand
There are increasing demands for "causal ML models" of the agent behaviors, which enable us to unbox the complex black-box models and make inferences or do proper counterfactual simulations. Many applications (especially in Marketing) intrinsically call for measurement of the causal impact
From playlist Fundamentals of 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
Time Series Model Selection (AIC & BIC) : Time Series Talk
How do we pick between several possible time series models? Code used in this video: https://github.com/ritvikmath/Time-Series-Analysis/blob/master/Model%20Selection.ipynb Data used in this video: https://github.com/ritvikmath/Time-Series-Analysis/blob/master/catfish.csv
From playlist Time Series Analysis
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
Monte Carlo Simulation and Python
Monte Carlo Simulation with Python Playlist: http://www.youtube.com/watch?v=9M_KPXwnrlE&feature=share&list=PLQVvvaa0QuDdhOnp-FnVStDsALpYk2hk0 Here we bring at least the initial batch of tutorials to a close with the 3D plotting of our variables in search for preferable settings to use.
From playlist Monte Carlo Simulation with Python
Evaluating Time Series Models : Time Series Talk
How do we evaluate our time series models? How can we tell if one model is better than another?
From playlist Time Series Analysis
Simulation: The Challenge for Data Science
While machine learning has recently had dramatic successes, there is a large class of problems that it will never be able to address on its own. To test a policy proposal, for example, often requires understanding a counterfactual scenario that has never existed in the past, and that may
From playlist Turing Seminars
How to pick a machine learning model 2: Separating signal from noise
Part of the End-to-End Machine Learning School course library at http://e2eml.school See these concepts used in an End to End Machine Learning project: https://end-to-end-machine-learning.teachable.com/p/polynomial-regression-optimization/ Watch the rest of the How to Choose a Model serie
From playlist E2EML 171. How to Choose Model
Modeling Ten Helicopters at Once: Reusable and Flexible Modeling
Learn about the flexibility of modeling in SystemModeler and how to construct one model that allows evaluation of many different systems and scenarios. Download the Notebook: http://bit.ly/1SspC4I Watch on Wolfram: http://www.wolfram.com/broadcast/video.php?c=405&v=1539
From playlist Modeling and Simulation with Wolfram SystemModeler
Francis Hui - Spatial Confounding for GEEs
Dr Francis Hui (ANU) presents "Spatial Confounding for GEEs", 19 June 2020.
From playlist Statistics Across Campuses
DDPS | Empowering Hybrid Twins from Physics-Informed Artificial Intelligence
Talk Abstract World is changing very rapidly. Today we do not sell aircraft engines, but hours of flight, we do not sell an electric drill but good quality holes, … and so on. We are nowadays more concerned by performances than by the products themselves. Thus, the new needs imply focusi
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
Michael Hyland: "Integrating State-of-the-Art Mobility-on-Demand Fleet Models into Transportatio..."
Mathematical Challenges and Opportunities for Autonomous Vehicles 2020 Workshop III: Large Scale Autonomy: Connectivity and Mobility Networks "Integrating State-of-the-Art Mobility-on-Demand Fleet Models into Transportation System Simulation Tools for Policy Analysis" Michael Hyland - Uni
From playlist Mathematical Challenges and Opportunities for Autonomous Vehicles 2020
Statistical Rethinking 2023 - 09 - Modeling Events
Course details: https://github.com/rmcelreath/stat_rethinking_2023 Intro: https://www.youtube.com/watch?v=kFRdoYfZYUY River: https://www.youtube.com/watch?v=hh2Vs13sdNk Tide machine: https://www.youtube.com/watch?v=DmxLUb8g10Q Lego tide machine: https://www.youtube.com/watch?v=sAyVcM3g4q4
From playlist Statistical Rethinking 2023
Andrew Ferguson: "Machine learning-enabled enhanced sampling in biomolecular simulation and..."
Machine Learning for Physics and the Physics of Learning Tutorials 2019 "Machine learning-enabled enhanced sampling in biomolecular simulation and data-driven design of self-assembling photonic crystals and optoelectonic π-conjugated oligopeptides" Andrew Ferguson, University of Chicago -
From playlist Machine Learning for Physics and the Physics of Learning 2019
System Dynamics: Systems Thinking and Modeling for a Complex World
MIT RES.15-004 System Dynamics: Systems Thinking and Modeling for a Complex World, IAP 2020 Instructor: James Paine View the complete course: https://ocw.mit.edu/RES-15-004IAP20 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63Dur3imUjY08z92ypMphQ3 This one-day worksho
From playlist MIT OCW: RES.15-004 System Dynamics: Systems Thinking and Modeling for a Complex World, IAP 2020
"A Virtual Heart: Mathematical and Numerical Models of the Cardiac Electromechanical Function"
by Francesco Regazzoni (Politecnico di Milano, Italy)
From playlist Mathematical Biology
(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
Statistical Rethinking 2022 Lecture 10 - Counts & Confounds
Slides and other course materials: https://github.com/rmcelreath/stat_rethinking_2022 Music etc: Intro: https://www.youtube.com/watch?v=Qut2getKFT4 River Kelvin: https://www.youtube.com/watch?v=hh2Vs13sdNk Tide machine: https://www.youtube.com/watch?v=DmxLUb8g10Q Lego tides: https://www.y
From playlist Statistical Rethinking 2022