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 11.4) 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
If you are interested in learning more about this topic, please visit http://www.gcflearnfree.org/ to view the entire tutorial on our website. It includes instructional text, informational graphics, examples, and even interactives for you to practice and apply what you've learned.
From playlist Design Thinking
(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
If you are interested in learning more about this topic, please visit http://www.gcflearnfree.org/ to view the entire tutorial on our website. It includes instructional text, informational graphics, examples, and even interactives for you to practice and apply what you've learned.
From playlist Making Decisions
A hybrid decision making system using image analysis to detect human falls - Pingfan Wang
About the conference: The 1st International ‘Turing’ conference on decision support and recommender systems will bring together junior and experienced researchers, industry professionals and domain experts to discuss latest trends and ongoing challenges in: - Human and AI-driven complex
From playlist 1st International conference on decision support and recommender systems
The Explainer: What is a Business Model?
"Business model" and "strategy" are among the most sloppily used terms in business. --------------------------------------------------------------------- At Harvard Business Review, we believe in management. If the world’s organizations and institutions were run more effectively, if our
From playlist The Explainer
Python for Data Analysis: Decision Trees
This video covers the basics of decision trees and how to make decision trees for classification in Python. Subscribe: ► https://www.youtube.com/c/DataDaft?sub_confirmation=1 This is lesson 29 of a 30-part introduction to the Python programming language for data analysis and predictive m
From playlist Python for Data Analysis
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
How to use Decision Intelligence in 2022: Insights for human centered XAI - empirical study
Augment human decision-making. When do you trust AI? And why? Researchers from MIT and Auckland Univ found answers in their work on USABILITY Challenges of ML algorithms for DI (Decision Making): Partnering w/ US Child Protective Services (CPS) agencies a series of insights emerge about h
From playlist Explainable AI (XAI) and Decision Intelligence (DI). Performance on Vision.
The Dimpled Manifold Model of Adversarial Examples in Machine Learning (Research Paper Explained)
#adversarialexamples #dimpledmanifold #security Adversarial Examples have long been a fascinating topic for many Machine Learning researchers. How can a tiny perturbation cause the neural network to change its output by so much? While many explanations have been proposed over the years, t
From playlist Papers Explained
Stanford Seminar - WeBuildAI: Participatory framework for algorithmic governance
Min Kyung Lee Carnegie Mellon University January 18, 2019 Algorithms increasingly govern societal functions, impacting multiple stakeholders and social groups. How can we design these algorithms to balance varying interests and promote social welfare? As one response to this question, I p
From playlist Stanford Seminars
Python for Data Analysis: Random Forests
This video covers the basics of random forests and how to make random forest models for classification in Python. Subscribe: ► https://www.youtube.com/c/DataDaft?sub_confirmation=1 This is lesson 30 of a 30-part introduction to the Python programming language for data analysis and predic
From playlist Python for Data Analysis
Fairness and robustness in machine learning – a formal methods perspective - Aditya Nori, Microsoft
With the range and sensitivity of algorithmic decisions expanding at a break-neck speed, it is imperative that we aggressively investigate fairness and bias in decision-making programs. First, we show that a number of recently proposed formal definitions of fairness can be encoded as proba
From playlist Logic and learning workshop
What Were You Thinking? Decision Theory as Coherence Test - Prof. Itzhak Gilboa
Abstract This talk is based on the joint work with Prof. Larry Samuelson from the Department of Economics at Yale University and full text is available here: https://www.google.com/url?q=https%3A%2F%2Fitzhakgilboa.weebly.com%2Fuploads%2F8%2F3%2F6%2F3%2F8363317%2Fgs_decision_theory_coheren
From playlist Uncertainty and Risk
20 Data Analytics: Decision Tree
Lecture on decision tree-based machine learning with workflows in R and Python and linkages to bagging, boosting and random forest.
From playlist Data Analytics and Geostatistics
Bayesian Decision Flow Diagrams: An Agent Based Modeling....(Remote Talk) by Parantapa Bhattacharya
DISCUSSION MEETING THE THEORETICAL BASIS OF MACHINE LEARNING (ML) ORGANIZERS: Chiranjib Bhattacharya, Sunita Sarawagi, Ravi Sundaram and SVN Vishwanathan DATE : 27 December 2018 to 29 December 2018 VENUE : Ramanujan Lecture Hall, ICTS, Bangalore ML (Machine Learning) has enjoyed tr
From playlist The Theoretical Basis of Machine Learning 2018 (ML)
Design thinking can improve anything from a water bottle to a community water system. See how design thinking improves the creative process, from Professor Stefanos Zenios: http://stanford.io/1mgkHGR
From playlist More