Decision Model and Notation

(ML 11.2) Decision theory terminology in different contexts

Comparison of decision theory terminology and notation in three different contexts: in general, for estimators, and for regression/classification.

From playlist Machine Learning

Introduction to Decision Trees | Decision Trees for Machine Learning | Part 1

The decision tree algorithm belongs to the family of supervised learning algorithms. Just like other supervised learning algorithms, decision trees model relationships, and dependencies between the predictive outputs and the input features. As the name suggests, the decision tree algorit

From playlist Introduction to Machine Learning 101

(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

(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

(ML 2.1) Classification trees (CART)

Basic intro to decision trees for classification using the CART approach. A playlist of these Machine Learning videos is available here: http://www.youtube.com/my_playlists?p=D0F06AA0D2E8FFBA

From playlist Machine Learning

Understanding Function Notation f(x)

I define Function Notation and then work through 3 examples. Example 1 is a word problem where we write a function that models the real world setting and then use it to evaluate a total cost at 6:55 Example 2 and Example 3 are substituting either a value or an expression into a function an

From playlist SAT Concept Review

(ML 3.1) Decision theory (Basic Framework)

A simple example to motivate decision theory, along with definitions of the 0-1 loss and the square loss. A playlist of these Machine Learning videos is available here: http://www.youtube.com/my_playlists?p=D0F06AA0D2E8FFBA

From playlist Machine Learning

Decision-Making Strategies

In this video, you’ll learn strategies for making decisions large and small. Visit https://edu.gcfglobal.org/en/problem-solving-and-decision-making/ for our text-based tutorial. We hope you enjoy!

From playlist Making Decisions

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

12.2 Chris Mattmann (Part 2): Big Data Architecture: Fundamentals

From playlist Big Data Analytics - JPL/Caltech

14: Rate Models and Perceptrons - Intro to Neural Computation

MIT 9.40 Introduction to Neural Computation, Spring 2018 Instructor: Michale Fee View the complete course: https://ocw.mit.edu/9-40S18 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP61I4aI5T6OaFfRK2gihjiMm Explores a mathematically tractable model of neural networks, r

From playlist MIT 9.40 Introduction to Neural Computation, Spring 2018

SystemModeler: Introducing the Business Simulation Library

Wolfram System Modeler and Modelica--the modeling language behind System Modeler--are not widely known in the system dynamics community, which is predominantly occupied with modeling social systems to tackle business and public policy issues. The new Business Simulation library (BSL) emplo

From playlist Wolfram Technology Conference 2020

Lecture 5 - GDA & Naive Bayes | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018)

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3GfTLkU Andrew Ng Adjunct Professor of Computer Science https://www.andrewng.org/ To follow along with the course schedule and syllabus, visit: http://cs229.sta

From playlist Stanford CS229: Machine Learning Full Course taught by Andrew Ng | Autumn 2018

Introduction to Query-to-Communication Lifting - Mika Goos

Computer Science/Discrete Mathematics Seminar II Topic: Introduction to Query-to-Communication Lifting Speaker: Mika Goos Affiliation: Member, School of Mathematics Date: November 20, 2018 For more video please visit http://video.ias.edu

From playlist Mathematics

Professor Mike West: Structured Dynamic Graphical Models & Scaling Multivariate Time Series

The Turing Lectures - Professor Mike West: Structured Dynamic Graphical Models & Scaling Multivariate Time Series. Click the below timestamps to navigate the video. 00:00:12 Welcome & Introduction by Doctor Ioanna Manolopoulou 00:01:19 Professor Mike West: Structured Dynamic

From playlist Turing Lectures

Cross Validation, Neural Nets

We go over ways to implement cross validation, and begin working on neural networks.

From playlist MachineLearning

Lecture 6 - Support Vector Machines | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018)

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3Gchxyg Andrew Ng Adjunct Professor of Computer Science https://www.andrewng.org/ To follow along with the course schedule and syllabus, visit: http://cs229.sta

From playlist Stanford CS229: Machine Learning Full Course taught by Andrew Ng | Autumn 2018

Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 2 - Given a Model of the World

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai Professor Emma Brunskill, Stanford University https://stanford.io/3eJW8yT Professor Emma Brunskill Assistant Professor, Computer Science Stanford AI for Human

From playlist Stanford CS234: Reinforcement Learning | Winter 2019

Rémi Bardenet: A tutorial on Bayesian machine learning: what, why and how - lecture 1

HYBRID EVENT Recorded during the meeting "End-to-end Bayesian Learning Methods " the October 25, 2021 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide mathematicians on CIRM's

From playlist Mathematical Aspects of Computer Science

(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