Finding the Tallest Tree: comparing tree-based models
Tree-based models such as decision trees, random forests, and boosted trees provide powerful predictions and are fast to compute. There are many different ways to fit these models in R, including the rpart, randomForest, and xgboost packages. During this talk, we'll examine numerous ways t
From playlist Introduction to Machine Learning
Morphing Symmetric Binary Trees (visual calming for anxiety; bilateral stimulation)
A symmetric binary tree is obtained by applying certain affine linear transformations recursively to the leaves starting with a trunk of unit length. This video shows six different scale factors and morphs between various angles of rotation. The animation is set to Bilateral music to help
From playlist Fractals
Graph of x^2 + y^2 + pxy as p varies
From playlist 3d graphs
Introduction to Spanning Trees
This video introduces spanning trees. mathispower4u.com
From playlist Graph Theory (Discrete Math)
KD tree algorithm: how it works
[http://bit.ly/k-NN] K-D trees allow us to quickly find approximate nearest neighbours in a (relatively) low-dimensional real-valued space. The algorithm works by recursively partitioning the set of training instances based on a median value of a chosen attribute. When we get a new data in
From playlist Nearest Neighbour Methods
This shows a 3d printed mobile produced using shapeways.com. This is joint work with Marco Mahler. This is available at http://shpws.me/nPha.
From playlist 3D printing
37: Balancing - Richard Buckland UNSW
Comp1927 lecture 37 Balance. Implications of balanced and unbalanced tress. Also Teamwork. Blame. Complaining.
From playlist CS2: Data Structures and Algorithms - Richard Buckland
Local Statistics, Semidefinite Programming, and Community Detection - Prasad Raghavendra
Computer Science/Discrete Mathematics Seminar I Topic: Local Statistics, Semidefinite Programming, and Community Detection Speaker: Prasad Raghavendra Affiliation: University of California, Berkeley Date: May 4, 2020 For more video please visit http://video.ias.edu
From playlist Mathematics
12. Pseudorandom graphs II: second eigenvalue
MIT 18.217 Graph Theory and Additive Combinatorics, Fall 2019 Instructor: Yufei Zhao View the complete course: https://ocw.mit.edu/18-217F19 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP62qauV_CpT1zKaGG_Vj5igX What can be inferred about a graph from its second eigenv
From playlist MIT 18.217 Graph Theory and Additive Combinatorics, Fall 2019
MIT 6.006 Introduction to Algorithms, Fall 2011 View the complete course: http://ocw.mit.edu/6-006F11 Instructor: Srini Devadas License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 6.006 Introduction to Algorithms, Fall 2011
Transcience for the interchange process in dimension 5 - Allan Sly
Probability Seminar Topic: Transcience for the interchange process in dimension 5 Speaker: Allan Sly Affiliation: Princeton University Date: October 07, 2022 The interchange process \sigma_T is a random permutation valued process on a graph evolving in time by transpositions on its edge
From playlist Mathematics
20. Asynchronous Distributed Algorithms: Shortest-Paths Spanning Trees
MIT 6.046J Design and Analysis of Algorithms, Spring 2015 View the complete course: http://ocw.mit.edu/6-046JS15 Complete course playlist: https://www.youtube.com/watch?v=2P-yW7LQr08&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp Instructor: Nancy Ann Lynch In this lecture, Professor Lynch intro
From playlist MIT 6.046J Design and Analysis of Algorithms, Spring 2015
MIT 6.006 Introduction to Algorithms, Spring 2020 Instructor: Jason Ku View the complete course: https://ocw.mit.edu/6-006S20 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63EdVPNLG3ToM6LaEUuStEY This session focuses on preparing for the quiz. High-level concepts are
From playlist MIT 6.006 Introduction to Algorithms, Spring 2020
Michal Pilipczuk: Introduction to parameterized algorithms, lecture II
The mini-course will provide a gentle introduction to the area of parameterized complexity, with a particular focus on methods connected to (integer) linear programming. We will start with basic techniques for the design of parameterized algorithms, such as branching, color coding, kerneli
From playlist Summer School on modern directions in discrete optimization
Dynamic Programming Crash Course | Advanced Data Structures And Algorithms Tutorial | Simplilearn
🔥Post Graduate Program In Full Stack Web Development: https://www.simplilearn.com/pgp-full-stack-web-development-certification-training-course?utm_campaign=DynamicProgrammingCrashCourse-xZKqH7ZcS_Y&utm_medium=DescriptionFF&utm_source=youtube 🔥Caltech Coding Bootcamp (US Only): https://www.
From playlist Data Structures & Algorithms [2022 Updated]
See complete series on data structures here: http://www.youtube.com/playlist?list=PL2_aWCzGMAwI3W_JlcBbtYTwiQSsOTa6P In this lesson, we have discussed binary tree in detail. We have talked about different types of binary tree like "complete binary tree", "perfect binary tree" and "balance
From playlist Data structures
Viswanath Nagarajan: Approximation Friendly Discrepancy Rounding
We consider the general problem of rounding a fractional vector to an integral vector while (approximately) satisfying a number of linear constraints. Randomized rounding and discrepancy-based rounding are two of the strongest rounding methods known. However these algorithms are very diffe
From playlist HIM Lectures: Trimester Program "Combinatorial Optimization"