Binary decision diagram

(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 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

Binary Tree 1. Constructing a tree (algorithm and pseudocode)

This is the first in a series of videos about binary trees. It is an explanation of the dynamic data structure known as the Binary Tree. It describes the way in which a binary tree is constructed, and how it can be represented numerically using a system of left and right pointers. This v

From playlist Data Structures

Binary Genetic Algorithm - Part 3: Decision Variables, Costs, Population, Natural Selection Process

This video is about Binary Genetic Algorithm - Part 3: Decision Variables, Cost Functions and Population and Natural Selection Process

From playlist Optimization

Decision Trees (1)

Decision tree basics

From playlist cs273a

7. Viterbi decoding

MIT 6.02 Introduction to EECS II: Digital Communication Systems, Fall 2012 View the complete course: http://ocw.mit.edu/6-02F12 Instructor: George Verghese This lecture starts with a review of encoding and decoding. The Viterbi algorithm, which includes a branch netric and a path metric,

From playlist MIT 6.02 Introduction to EECS II: Digital Communication Systems, Fall 2012

Support Vector Machines - Part 5: Multi-class SVMs

This video is about Support Vector Machines - Part 5: Multi-class SVMs Abstract: This is a series of videos about Support Vector Machines (SVMs), which will walk through the introduction, the working principle and theory covering a linearly separable case, non-separable case, nonlinear S

From playlist Machine Learning

Nexus Trimester - Paul Beame (University of Washington) - 1

Branching Programs 1/3 Paul Beame (University of Washington) February 26,2016 Abstract: Branching programs are clean and simple non-uniform models of computation that capture both time and space simultaneously. We present the best methods known for obtaining lower bounds on the size of (l

From playlist Nexus Trimester - 2016 - Fundamental Inequalities and Lower Bounds Theme

Vintage 1962 "Digital Computer Techniques" - core memory, magnetic storage, etc.

Original un-edited 1962 film. A “somewhat dry” Army/Navy film of basic computer concepts. Detailed descriptions & diagrams of computing “input, store, control, arithmetic, output”, etc. Machine peripherals shown briefly. Film quality starts poor, but gets better towards the end. Nice d

From playlist Computers of the 1960's

An introduction to Decision Trees - #SoME2

The Python code used to make the animations for the video can be found at https://github.com/Elektriman/SOME2_decisionTrees. For any question or request about this code, feel free to send an email at julien.crambes@gmail.com or a private message on GitHub. If you have questions about the

From playlist Summer of Math Exposition 2 videos

Introduction to Detection Theory (Hypothesis Testing)

http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. Includes definitions of binary and m-ary tests, simple and composite hypotheses, decision regions, and test performance characterization: prob

From playlist Estimation and Detection Theory

Lecture 2/16 : The Perceptron learning procedure

Neural Networks for Machine Learning by Geoffrey Hinton [Coursera 2013] 2A An overview of the main types of neural network architecture 2B Perceptrons: The first generation of neural networks 2C A geometrical view of perceptrons 2D Why the learning works 2E What perceptrons can't do

From playlist Neural Networks for Machine Learning by Professor Geoffrey Hinton [Complete]

Machine Learning Interview Questions And Answers | Data Science Interview Questions | Simplilearn

🔥 Enroll for FREE Machine Learning Course & Get your Completion Certificate: https://www.simplilearn.com/learn-machine-learning-basics-skillup?utm_campaign=MachineLearning&utm_medium=Description&utm_source=youtube This Machine Learning Interview Questions And Answers video will help you

From playlist Machine Learning with Python | Complete Machine Learning Tutorial | Simplilearn [2022 Updated]

Determine if the Binary Operation Defined by the Table is Commutative and Associative

In this video we determine whether or not a binary operation is commutative and associative. The binary operation is actually defined by a table in this example. I hope this video helps someone.

From playlist Abstract Algebra

Stanford Lecture: Donald Knuth - "Bayesian trees and BDDs" (2011)

December 8th, 2011 Professor Donald Knuth's 17th annual Christmas Tree Lecture. Knuth explains how to apply elementary BDD technology so that the probability of such events (and many others) can be computed in polynomial time. Learn more: http://scpd.stanford.edu/knuth/index.jsp

From playlist Donald Knuth Lectures

Examples of Binary Operations (and Non-Examples) | Abstract Algebra

What are binary operations? A binary operation is a function from the cartesian product of a set with itself back to that same set. In other words, a binary operations takes two elements from the same set and assigns the ordered pair of them to exactly one element also in that set (since i

From playlist Abstract Algebra

Design Principles of Large Cell-Fate Decision-Making Regulatory Networks by Mohit Kumar Jolly

PROGRAM STATISTICAL BIOLOGICAL PHYSICS: FROM SINGLE MOLECULE TO CELL ORGANIZERS: Debashish Chowdhury (IIT-Kanpur, India), Ambarish Kunwar (IIT-Bombay, India) and Prabal K Maiti (IISc, India) DATE: 11 October 2022 to 22 October 2022 VENUE: Ramanujan Lecture Hall 'Fluctuation-and-noise' a

From playlist STATISTICAL BIOLOGICAL PHYSICS: FROM SINGLE MOLECULE TO CELL (2022)

Symmetric Binary Trees (visual construction)

In this video, we see how to change use two parameters (scale factor and angle of rotation) to create various symmetric binary trees. We show five different examples of such trees (up to level 13). Choose your own parameters and create your own! Check out these videos for related construc

From playlist Fractals

Eun-Ah Kim - Machine Learning for Quantum Simulation - IPAM at UCLA

Recorded 15 April 2022. Eun-Ah Kim of Cornell University presents "Machine Learning for Quantum Simulation" at IPAM's Model Reduction in Quantum Mechanics Workshop. Learn more online at: http://www.ipam.ucla.edu/programs/workshops/workshop-ii-model-reduction-in-quantum-mechanics/?tab=sched

From playlist 2022 Model Reduction in Quantum Mechanics Workshop