(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
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
(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 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
From playlist Decision Tree Learning
(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
This is How You Use the Chain Rule in Calculus
This is How You Use the Chain Rule in Calculus
From playlist Random calculus problems:)
A02 Independence of the solution set
The independence of a linear system. How to make sure that a set of solutions are not constant multiples of each other.
From playlist A Second Course in Differential Equations
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
Automating Annotation Process Using Rule-Based Algorithm
Install NLP Libraries https://www.johnsnowlabs.com/install/ Register for Healthcare NLP Summit 2023: https://www.nlpsummit.org/#register Watch all NLP Summit 2022 sessions: https://www.nlpsummit.org/nlp-summit-2022-watch-now/ Presented by Priya Shaji, Data Scientist at MEMORIAL SLOAN
From playlist NLP Summit 2022
Explaining generalisation and individual justice, Reuben Binns
- Links to talks - Reuben Binns: https://youtu.be/VoPSvQYeYpI Alison Reuben: https://youtu.be/btUxLhTPvUQ David Leslie: https://youtu.be/F4G0_01kAN4 Panel discussion: https://youtu.be/WM8BmRkIXX0 In the lecture series 'Driving data futures', the public policy programme of The Alan Turing
From playlist Driving Data Futures: AI explainability with a human face
Oktay Günlük: "Fair and Interpretable Decision Rules for Binary Classification"
Deep Learning and Combinatorial Optimization 2021 "Fair and Interpretable Decision Rules for Binary Classification" Oktay Günlük - Cornell University Abstract: In this talk we consider the problem of building Boolean rule sets in disjunctive normal form (DNF), an interpretable model for
From playlist Deep Learning and Combinatorial Optimization 2021
Re-Imagining the Social Sciences in the Age of AI - March 4, 2020
Re-Imagining the Social Sciences in the Age of AI: A Cross-Disciplinary Conversation Wednesday, March 4 5:30 p.m. Wolfensohn Hall Co-organized by the School of Mathematics and the School of Social Sciences, this public event will feature two short talks about the transformational possibi
From playlist Mathematics
Ruby Conf 12 - Machine Learning for Fun and Profit by Chris Nelson
TDD is a great way to test code, but have you ever wondered if there are ways to leverage the awesome power of computers and help us write better tests? Research in the field of formal verification has shown promising results with tools that analyze programs for logic errors and can even f
From playlist Ruby Conference 2012
Assessing Game Balance with AlphaZero: Exploring Alternative Rule Sets in Chess (Paper Explained)
#ai #chess #alphazero Chess is a very old game and both its rules and theory have evolved over thousands of years in the collective effort of millions of humans. Therefore, it is almost impossible to predict the effect of even minor changes to the game rules, because this collective proce
From playlist Reinforcement Learning
01 Decision analysis as a science
Introduction to decision making under uncertainty
From playlist QUSS GS 260
Decoding the Science of Decision Trees! Learn from Experts | Webinar -1 | Edureka
Watch Sample Class recording: http://goo.gl/OBlNnC This course is designed for professionals who aspire to learn 'R' language for Analytics. The course starts from the very basics like: Introduction to R programming, how to import various formats of Data, manipulate it, etc. to advanced t
From playlist Webinars by Edureka!
23. Democracy and Majority Rule (II)
Moral Foundations of Politics (PLSC 118) Majority rule and democratic competition serve as the focus of this, second lecture on the democratic tradition. What it is about majority rule that confers legitimacy on collective decisions. Is there validity to a utilitarian justification, tha
From playlist The Moral Foundations of Politics with Ian Shapiro
Learning To See [Part 15: Information]
In this series, we'll explore the complex landscape of machine learning and artificial intelligence through one example from the field of computer vision: using a decision tree to count the number of fingers in an image. It's gonna be crazy. Supporting Code: https://github.com/stephencwe
From playlist Learning To See
The Product Rule of Differentiation (Introduction)
This video is a new version of the introductory video to the product rule of differentiation. Site: http://mathispower4u.com Search: http://mathispower4u.wordpress.com
From playlist Differentiation Using the Product Rule