(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
Causal Behavioral Modeling Framework - Discrete Choice Modeling of Consumer Demand
There are increasing demands for "causal ML models" of the agent behaviors, which enable us to unbox the complex black-box models and make inferences or do proper counterfactual simulations. Many applications (especially in Marketing) intrinsically call for measurement of the causal impact
From playlist Fundamentals of 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
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
A Spirit of Trust: Magnanimity and Agency in Hegel’s Phenomenology
Robert Brandom is Distinguished Professor of Philosophy and Fellow at the Center for Philosophy of Science at the University of Pittsburgh. He is the author of thirteen books, including Making It Explicit: Reasoning, Representing, and Discursive Commitment. His most recent book, A Spirit o
From playlist Franke Lectures in the Humanities
Marcelo Pereyra: Bayesian inference and mathematical imaging - Lecture 1: Bayesian analysis...
Bayesian inference and mathematical imaging - Part 1: Bayesian analysis and decision theory Abstract: This course presents an overview of modern Bayesian strategies for solving imaging inverse problems. We will start by introducing the Bayesian statistical decision theory framework underp
From playlist Probability and Statistics
Statistical and Computational Results involving Optimal Transport... by Jose Blanchet
PROGRAM: ADVANCES IN APPLIED PROBABILITY ORGANIZERS: Vivek Borkar, Sandeep Juneja, Kavita Ramanan, Devavrat Shah, and Piyush Srivastava DATE & TIME: 05 August 2019 to 17 August 2019 VENUE: Ramanujan Lecture Hall, ICTS Bangalore Applied probability has seen a revolutionary growth in resear
From playlist Advances in Applied Probability 2019
The Spontaneous Emergence of Collective Behavior
From the mediaX Seminar, Science Storytelling & the Power of Participation; Damon Centola discusses the impact of network “topology” – the graph-theoretic structure of social ties in a network – and “homophily” – the similarity or differences between connected members of a population – on
From playlist Science Storytelling and the Power of Participation
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
Optimal Transport Methods and Applications to Statistics and... (Lecture 3) by Jose Blanchet
PROGRAM: ADVANCES IN APPLIED PROBABILITY ORGANIZERS: Vivek Borkar, Sandeep Juneja, Kavita Ramanan, Devavrat Shah, and Piyush Srivastava DATE & TIME: 05 August 2019 to 17 August 2019 VENUE: Ramanujan Lecture Hall, ICTS Bangalore Applied probability has seen a revolutionary growth in resear
From playlist Advances in Applied Probability 2019
Threat and Use of Force by Nikita Kohli
Program Summer Research Program on Dynamics of Complex Systems ORGANIZERS: Amit Apte, Soumitro Banerjee, Pranay Goel, Partha Guha, Neelima Gupte, Govindan Rangarajan and Somdatta Sinha DATE : 15 May 2019 to 12 July 2019 VENUE : Madhava hall for Summer School & Ramanujan hall f
From playlist Summer Research Program On Dynamics Of Complex Systems 2019
Habermas on the Concept of Practical Reason (1988)
Jürgen Habermas gives a 1988 talk at Berkeley on the concept of practical reason (pragmatic, ethical, and moral uses). Once again, I tried to transcribe the lecture in order to make it easier to follow. It should be fairly good, but it is likely not free of errors. Note, the introduction h
From playlist Social & Political Philosophy
Beyond Blameless - Rein Henrichs - REdeploy 2019
In the aftermath of John Allspaw’s influential Blameless Post-Mortems, blamelessness has become a shibboleth for modern production operations teams: Is our culture blameless? Are our incident reviews blameless? But it seems that something has been lost in translation. Organizations that t
From playlist REdeploy 2019
Simon Lacoste-Julien: Apprentissage statistique et big data : notions de base pour l'analyse [...]
Find this video and other talks given by worldwide mathematicians on CIRM's Audiovisual Mathematics Library: http://library.cirm-math.fr. And discover all its functionalities: - Chapter markers and keywords to watch the parts of your choice in the video - Videos enriched with abstracts, b
From playlist Mathematical Aspects of Computer Science
Set Distribution Networks: a Generative Model for Sets of Images (Paper Explained)
We've become very good at making generative models for images and classes of images, but not yet of sets of images, especially when the number of sets is unknown and can contain sets that have never been encountered during training. This paper builds a probabilistic framework and a practic
From playlist Papers Explained