(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
How to Determine if Functions are Linearly Independent or Dependent using the Definition
How to Determine if Functions are Linearly Independent or Dependent using the Definition If you enjoyed this video please consider liking, sharing, and subscribing. You can also help support my channel by becoming a member https://www.youtube.com/channel/UCr7lmzIk63PZnBw3bezl-Mg/join Th
From playlist Zill DE 4.1 Preliminary Theory - Linear Equations
Determine if the Functions are Linearly Independent or Linearly Dependent
Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys How to determine if three functions are linearly independent or linearly dependent using the definition.
From playlist Differential Equations
Powered by https://www.numerise.com/ Formulating a linear programming problem
From playlist Linear Programming - Decision Maths 1
Differential Equations: Linear Independence
Linear independence is a core idea from Linear Algebra. Surprisingly, it's also important in differential equations. This video is the second precursor to our discussion of homogeneous differential equations.
From playlist Differential Equations
Linear Algebra: Linear Independence Problems
In this video, I work through several practice problems relating to the concept of linear independence. These including using the definition of linear independence, as well as "shortcuts" to determine whether a set is linearly independent without solving a vector equation.
From playlist Linear Algebra Lectures
Determining if Functions are Linearly Independent or Dependent using the Definition
Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys Determining if Functions are Linearly Independent or Dependent using the Definition
From playlist Differential Equations
Paul Grigas - Offline and Online Learning for Contextual Stochastic Optimization - IPAM at UCLA
Recorded 03 March 2023. Paul Grigas of the University of California, Berkeley, presents "Offline and Online Learning for Contextual Stochastic Optimization" at IPAM's Artificial Intelligence and Discrete Optimization Workshop. Abstract: Often the parameters of an optimization task are pred
From playlist 2023 Artificial Intelligence and Discrete Optimization
Provably Efficient Reinforcement Learning with Linear Function Approximation - Chi Jin
Workshop on Theory of Deep Learning: Where next? Topic: Provably Efficient Reinforcement Learning with Linear Function Approximation Speaker: Chi Jin Affiliation: Member, School of Mathematics Date: October 17, 2019 For more video please visit http://video.ias.edu
From playlist Mathematics
Bistability: a building block for cellular decisions by Sandeep Krishna
ORGANIZERS : Vidyanand Nanjundiah and Olivier Rivoire DATE & TIME : 16 April 2018 to 26 April 2018 VENUE : Ramanujan Lecture Hall, ICTS Bangalore This program is aimed at Master's- and PhD-level students who wish to be exposed to interesting problems in biology that lie at the biology-
From playlist Living Matter 2018
(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
Some Theoretical Results on Model-Based Reinforcement Learning by Mengdi Wang
Program Advances in Applied Probability II (ONLINE) ORGANIZERS: Vivek S Borkar (IIT Bombay, India), Sandeep Juneja (TIFR Mumbai, India), Kavita Ramanan (Brown University, Rhode Island), Devavrat Shah (MIT, US) and Piyush Srivastava (TIFR Mumbai, India) DATE & TIME 04 January 2021 to
From playlist Advances in Applied Probability II (Online)
Math 060 092717 Linear Independence
Linear independence: definition of, examples and non-examples; intuition (dependence is redundancy; independence is minimality). Equivalence of dependence and a vector being included in the span of the others. Equivalence of independence with every vector in the span being uniquely expre
From playlist Course 4: Linear Algebra (Fall 2017)
Fundamental Machine Learning Algorithms - Linear Regression
The code is accessible at https://github.com/sepinouda/Machine-Learning/
From playlist Machine Learning Course
(ML 9.3) Choosing f under linear regression
Deriving the optimal prediction function f(x)=y under square loss. A playlist of these Machine Learning videos is available here: http://www.youtube.com/view_play_list?p=D0F06AA0D2E8FFBA
From playlist Machine Learning
Lecture 7 - Kernels | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018)
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3GftN16 Andrew Ng Adjunct Professor of Computer Science https://www.andrewng.org/ To follow along with the course schedule and syllabus, visit: http://cs229.sta
From playlist Stanford CS229: Machine Learning Full Course taught by Andrew Ng | Autumn 2018
Twenty third SIAM Activity Group on FME Virtual Talk Series
Date: Thursday, December 2, 2021, 1PM-2PM ET Speaker 1: Renyuan Xu, University of Southern California Speaker 2: Philippe Casgrain, ETH Zurich and Princeton University Moderator: Ronnie Sircar, Princeton Universit Join us for a series of online talks on topics related to mathematical fina
From playlist SIAM Activity Group on FME Virtual Talk Series
“Choice Modeling and Assortment Optimization” – Session III – Prof. Huseyin Topaloglu
This module overviews static and dynamic assortment optimization problems. We will start with an introduction to discrete choice modeling and discuss estimation issues when fitting a choice model to observed sales histories. Following this introduction, we will discuss static and dynamic a
From playlist Thematic Program on Stochastic Modeling: A Focus on Pricing & Revenue Management
Differential Equations: Linearity
Linearity is crucial throughout mathematics. In this video, I demonstrate the linearity of linear differential equations and explain why it can be useful. This video is the first precursor to our discussion of homogeneous differential equations.
From playlist Differential Equations
Boosting Simple Learners - Shay Moran
Seminar on Theoretical Machine Learning Topic: Boosting Simple Learners Speaker: Shay Moran Affiliation: Google Date: May 5, 2020 For more video please visit http://video.ias.edu
From playlist Mathematics