(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
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 11.2) 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
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
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 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
(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
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
Data Science - Part V - Decision Trees & Random Forests
For downloadable versions of these lectures, please go to the following link: http://www.slideshare.net/DerekKane/presentations https://github.com/DerekKane/YouTube-Tutorials This lecture provides an overview of decision tree machine learning algorithms and random forest ensemble techniq
From playlist Data Science
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
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
Introduction to R: Decision Trees
This lesson covers the basics of decision trees in R. This is lesson 29 of a 30-part introduction to the R programming language for data analysis and predictive modeling. Link to the code notebook below: Intro to R: Decision Trees https://www.kaggle.com/hamelg/intro-to-r-part-29-Decision
From playlist Introduction to R
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
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)
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
Decision trees are powerful and surprisingly straightforward. Here's how they are grown. Code: https://github.com/brohrer/brohrer.github.io/blob/master/code/decision_tree.py Slides: https://docs.google.com/presentation/d/1fyGhGxdGcwt_eg-xjlMKiVxstLhw42XfGz3wftSzRjc/edit?usp=sharing PERM
From playlist Data Science
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