Decision problem

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

(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

B06 Example problem with separable variables

Solving a differential equation by separating the variables.

From playlist Differential Equations

C49 Example problem solving a system of linear DEs Part 1

Solving an example problem of a system of linear differential equations, where one of the equations is not homogeneous. It's a long problem, so this is only part 1.

From playlist Differential Equations

B04 Example problem with separable variables

Solving a differential equation by separating the variables.

From playlist Differential Equations

(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

Critical Thinking

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 Critical Thinking

B15 Example problem with a linear equation using the error function

Solving an example problem for a linear equation with the error function.

From playlist Differential Equations

C51 Example problem of a system of linear DEs

Example problem solving a system of linear differential equations.

From playlist Differential Equations

Value of Information in the Earth Sciences

Overview, narrated by Tapan Mukerji Eidsvik, J., Mukerji, T. and Bhattacharjya, D., 2015. Value of information in the earth sciences: Integrating spatial modeling and decision analysis. Cambridge University Press.

From playlist Uncertainty Quantification

Phebe Vayanos - Integer optimization for predictive & prescriptive analytics in high stakes domains

Recorded 01 March 2023. Phebe Vayanos of the University of Southern California presents "Integer optimization for predictive and prescriptive analytics in high stakes domains" at IPAM's Artificial Intelligence and Discrete Optimization Workshop. Abstract: Motivated by problems in homeless

From playlist 2023 Artificial Intelligence and Discrete Optimization

Priya Donti - Optimization-in-the-loop AI for energy and climate - IPAM at UCLA

Recorded 28 February 2023. Priya Donti of Cornell University presents "Optimization-in-the-loop AI for energy and climate" at IPAM's Artificial Intelligence and Discrete Optimization Workshop. Abstract: Addressing climate change will require concerted action across society, including the d

From playlist 2023 Artificial Intelligence and Discrete Optimization

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

Lecture 33 | Decision Theory 3: Optimal Policies for Sequential Decisions

From playlist UBC: Introduction to Artificial Intelligence with Alan Mackworth | CosmoLearning.org Computer Science

Hamsa Bastani - Decision-Aware Learning for Global Health Supply Chains - IPAM at UCLA

Recorded 01 March 2023. Hamsa Bastani of the University of Pennsylvania presents "Decision-Aware Learning for Global Health Supply Chains" at IPAM's Artificial Intelligence and Discrete Optimization Workshop. Abstract: The combination of machine learning (for prediction) and optimization (

From playlist 2023 Artificial Intelligence and Discrete Optimization

Lecture 11 | Programming Abstractions (Stanford)

Lecture 11 by Julie Zelenski for the Programming Abstractions Course (CS106B) in the Stanford Computer Science Department. Julie continues with recursive backtracking and introduces pointers and recursive data. Following, she focuses on solving the problems rather than the exact code

From playlist Lecture Collection | Programming Abstractions

01 Decision analysis as a science

Introduction to decision making under uncertainty

From playlist QUSS GS 260

Bistra Dilkina: "Decision-focused learning: integrating downstream combinatorics in ML"

Deep Learning and Combinatorial Optimization 2021 "Decision-focused learning: integrating downstream combinatorics in ML" Bistra Dilkina - University of Southern California (USC) Abstract: Closely integrating ML and discrete optimization provides key advantages in improving our ability t

From playlist Deep Learning and Combinatorial Optimization 2021

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

B07 Example problem with separable variables

Solving a differential equation by separating the variables.

From playlist Differential Equations