(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
01 Decision analysis as a science
Introduction to decision making under uncertainty
From playlist QUSS GS 260
(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
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 11.4) 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
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
In this video, you’ll strategies to improve your critical thinking skills. Visit https://edu.gcfglobal.org/en/problem-solving-and-decision-making/what-is-critical-thinking/1/ to learn even more. We hope you enjoy!
From playlist Critical Thinking
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
RubyConf 2017: Augmenting Human Decision Making with Data Science by Kelsey Pedersen
Augmenting Human Decision Making with Data Science by Kelsey Pedersen Humans and data science are flawed on their own. Humans lack the ability to process large volumes of information. Machines lack intuition, empathy and nuance. You’ll learn how to guide users of expert-use systems by app
From playlist RubyConf 2017
Decision Analytic Principles For Data Scientists | Data Science Predictive Modeling | Simplilearn
Today, the most valuable companies are those that successfully monetize data. As the global store of data dramatically grows, pressure increases for data scientists to improve at delivering value. Data scientists with this skill will enjoy an advantage over those who lack it. Because pred
From playlist Simplilearn Live
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
Business Analytics With R | Data Science Tutorial | Simplilearn
🔥 Business Analyst Master's Program (Discount Coupon: YTBE15): https://www.simplilearn.com/business-analyst-certification-training-course?utm_campaign=BusinessAnalytticswithR-GV7ID_CauJE&utm_medium=DescriptionFF&utm_source=youtube 🔥 Professional Certificate Program In Business Analysis: ht
From playlist R Programming For Beginners [2022 Updated]
Translating Data into Effective Decisions
As data scientists, we are constantly focused on learning new ML techniques and algorithms. However, in any company, value is created primarily by making decisions. In this talk I present a systematic process where ML is an input to improve our ability to make better decisions, thereby t
From playlist Data Analytics Tutorials
Data vs Creativity: The Last Battleground? - David Boyle keynote
From Strata + Hadoop 2015 NYC: With self-driving cars and delivery drones becoming a reality, it’s clear that data is on a relentless march to conquer industries one-by-one. But some industries have been largely successful in fending off the attack: despite shiny case studies of data use t
From playlist Strata Conference + Hadoop World 2015 (New York City)
Data science explained | Learning to code for data science beginners - Programming concepts playlist
This discussion is all about data science and programming for data science. Before we begin, let's look at how we will break things down. There are 3 topics that will lead us to an understanding of data science. 1) What is data science? 2) Data science project for trading stocks. 3) How m
From playlist Data Science - Learn to code for beginners
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
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