Graph Data Structure 6. The A* Pathfinding Algorithm
This is the sixth in a series of videos about the graph data structure. It includes a step by step walkthrough of the A* pathfinding algorithm (pronounced A Star) for a weighted, undirected graph. The A* pathfinding algorithm, and its numerous variations, is widely used in applications suc
From playlist Path Finding Algorithms
Support vector machines were all the rage in the 90s and they've become sort of old news since the advent of deep learning. However, they are still powerful regression and classification algorithms that work well as classical models for smaller datasets. This video describes the fundamenta
From playlist Materials Informatics
Robust Regression with the L1 Norm [Python]
This video discusses how least-squares regression is fragile to outliers, and how we can add robustness with the L1 norm. (Code in Python) Book Website: http://databookuw.com Book PDF: http://databookuw.com/databook.pdf These lectures follow Chapter 3 from: "Data-Driven Science and Eng
From playlist Sparsity and Compression [Data-Driven Science and Engineering]
Fundamental Machine Learning Algorithms - SVM & kNN
The code is accessible at https://github.com/sepinouda/Machine-Learning/
From playlist Machine Learning Course
Robust Regression with the L1 Norm
This video discusses how least-squares regression is fragile to outliers, and how we can add robustness with the L1 norm. Book Website: http://databookuw.com Book PDF: http://databookuw.com/databook.pdf These lectures follow Chapter 3 from: "Data-Driven Science and Engineering: Machin
From playlist Sparsity and Compression [Data-Driven Science and Engineering]
Vector and matrix forms for systems of linear equations | Linear Algebra MATH1141 | N J Wildberger
A system of linear equations may also be viewed in vector form, as an attempt to write one vector as a linear combination of other vectors. Or it more alternatively be viewed in matrix form. We discuss the matrix of coefficients, the vector of variables and the vector of constants. Puttin
From playlist Higher Linear Algebra
Google and eigenvalues. We describe the Pagerank algorithm, which was one of the algorithms used by Google for their search engine. For this, we rank the websites using an importance vector vector and write the system as a Markov chain, using matrices. Then we diagonalize the matrix using
From playlist Eigenvalues
Pierre Vandergheynst: Machine learning on graphs
CONFERENCE Recorded during the meeting "Theoretical Computer Science Spring School: Machine Learning" the May 23, 2022 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide mathemat
From playlist Mathematical Aspects of Computer Science
Satoru Fujishige: Combinatorial Polynomial Algorithms for Skew bisubmodular Function Minimization
Huber, Krokhin, and Powell (2013) introduced a concept of skew bisubmodularity, as a generalization of bisubmodularity, in their complexity dichotomy theorem for valued constraint satisfaction problems over the three-value domain, and they showed the oracle tractability of minimization of
From playlist HIM Lectures 2015
Raúl Tempone: Adaptive strategies for Multilevel Monte Carlo
Abstract: We will first recall, for a general audience, the use of Monte Carlo and Multi-level Monte Carlo methods in the context of Uncertainty Quantification. Then we will discuss the recently developed Adaptive Multilevel Monte Carlo (MLMC) Methods for (i) It Stochastic Differential Equ
From playlist Probability and Statistics
Ensemble techniques leverage many weak learners to create a strong learner! This video describes the basic principle, variance/bias tradeoff, homogeneous/heterogenous ensembles, bagging vs boosting vs stacking and some detailed walkthroughs of decision trees, random forests, adaboost, grad
From playlist Materials Informatics
Jørgen Bang-Jensen: Antistrong digraphs
Jørgen Bang-Jensen: Antistrong digraphs An antidirected trail in a digraph is a trail (a walk with no arc repeated) in which the arcs alternate between forward and backward arcs. An antidirected path is an antidirected trail where no vertex is repeated. We show that one can decide in line
From playlist HIM Lectures 2015
David McAllester - Dependent Type Theory from the Perspective of Mathematics, Physics, and (...)
Dependent type theory imposes a type system on Zemelo-Fraenkel set theory (ZFC). From a mathematics and physics perspective dependent type theory naturally generalizes the Bourbaki notion of structure and provides a universal notion of isomorphism and symmetry. This comes with a universal
From playlist Mikefest: A conference in honor of Michael Douglas' 60th birthday
Paths and cycles in expanders - Michael Krivelevich
Computer Science/Discrete Mathematics Seminar I Topic: Paths and cycles in expanders Speaker: Michael Krivelevich Affiliation: Tel Aviv University Date: February 10, 2020 For more video please visit http://video.ias.edu
From playlist Mathematics
GRCon21 - Interior Target Tracking Using Digital Communications Signals for Bistatic Radar ....
Interior Target Tracking Using Digital Communications Signals for Bistatic Radar with Gnu Radio presented by Todd K. Moon, Thomas Bradshaw, Mirelle Despain, Jacob H. Gunther at GNU Radio Conference 2021 Wireless communication systems employed in buildings (such as WiFi) can be used for bi
From playlist GRCon 2021
Robust Principal Component Analysis (RPCA)
Robust statistics is essential for handling data with corruption or missing entries. This robust variant of principal component analysis (PCA) is now a workhorse algorithm in several fields, including fluid mechanics, the Netflix prize, and image processing. Book Website: http://databoo
From playlist Data-Driven Science and Engineering
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
Network Analysis. Lecture 8. Network communitites
Cohesive subgroups. Graph cliques, k-plexes, k-cores. Network communities. Vertex similarity matrix. Similarity based clustering. Agglomerative clustering. Graph partitioning. Repeated bisection. Edge Betweenness. Newman-Girvin algorithm. Lecture slides: http//www.leonidzhukov.net/hse/201
From playlist Structural Analysis and Visualization of Networks.
Gradient Boost Part 1 (of 4): Regression Main Ideas
Gradient Boost is one of the most popular Machine Learning algorithms in use. And get this, it's not that complicated! This video is the first part in a series that walks through it one step at a time. This video focuses on the main ideas behind using Gradient Boost to predict a continuous
From playlist StatQuest