Clustering is the process of grouping a set of data given a certain criterion. In this way it is possible to define subgroups of data, called clusters, that share common characteristics. Determining the internal structure of the data is important in exploratory data analysis, but is also u
From playlist “How To” with MATLAB and Simulink
Top K Problem - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
(ML 16.2) K-means clustering (part 2)
Introduction to the K-means algorithm for clustering.
From playlist Machine Learning
Adam Polak: Nearly-Tight and Oblivious Algorithms for Explainable Clustering
We study the problem of explainable clustering in the setting first formalized by Dasgupta, Frost, Moshkovitz, and Rashtchian (ICML 2020). A k-clustering is said to b e explainable if it is given by a decision tree where each internal no de splits data points with a threshold cut in a sing
From playlist Workshop: Approximation and Relaxation
Clustering (3): K-Means Clustering
The K-Means clustering algorithm. Includes derivation as coordinate descent on a squared error cost function, some initialization techniques, and using a complexity penalty to determine the number of clusters.
From playlist cs273a
(ML 16.1) K-means clustering (part 1)
Introduction to the K-means algorithm for clustering.
From playlist Machine Learning
Clustering 8: alignment and pair-based evaluation
Full lecture: http://bit.ly/K-means To evaluate our clustering intrinsically, we need to either align system clusters to reference clusters, or use a pair-based evaluation. Alignment-based evaluation is popular, but has a number of undesirable properties. Pair-based evaluation is a bit m
From playlist K-means Clustering
From playlist k-Nearest Neighbor Algorithm
Randomized Top K - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
What We've Learned from NKS Chapter 2: The Crucial Experiment
In this episode of "What We've Learned from NKS", Stephen Wolfram is counting down to the 20th anniversary of A New Kind of Science with [another] chapter retrospective. If you'd like to contribute to the discussion in future episodes, you can participate through this YouTube channel or th
From playlist Science and Research Livestreams
Marina Meilă: "Validation and Reproducibility by Geometry, for Unsupervised Learning"
Machine Learning for Physics and the Physics of Learning 2019 Workshop III: Validation and Guarantees in Learning Physical Models: from Patterns to Governing Equations to Laws of Nature "Validation and Reproducibility by Geometry, for Unsupervised Learning" Marina Meilă - University of Wa
From playlist Machine Learning for Physics and the Physics of Learning 2019
Materials Project Seminars – Taylor Sparks, "Materials informatics: Moving beyond screening [...]"
Title: "Materials informatics: Moving beyond screening via generative machine learning models" Presented as a Materials Project Seminar on March 25th 2022. More information on the Materials Project Seminar Series at https://materialsproject.org/seminars including free registration links
From playlist Materials Informatics
DSC - Judy Qiu gives a talk about Cloud Computing Platforms
Judy Qiu gives a talk about cloud computing platforms at the Big Data for Science workshop held at the Pervasive Technology Institute, Indiana University. This event was put on by PTI's Digital Science Center July 26th - July 30th, 2010. For slides from this video or more information ab
From playlist Digital Science Center (DSC)
Tina Eliassi-Rad - The Pitfalls of Using ML-based Optimization - IPAM at UCLA
Recorded 03 March 2023. Tina Eliassi-Rad of Northeastern University, Computer Science & Network Science presents "The Pitfalls of Using ML-based Optimization" at IPAM's Artificial Intelligence and Discrete Optimization Workshop. Abstract: I will describe two graph problems where ML-based o
From playlist 2023 Artificial Intelligence and Discrete Optimization
Gianni De Fabritiis: "Machine Learning for Drug Design"
Machine Learning for Physics and the Physics of Learning Tutorials 2019 "Machine Learning for Drug Design" Gianni De Fabritiis, Universitat Pompeu Fabra Institute for Pure and Applied Mathematics, UCLA September 6, 2019 For more information: http://www.ipam.ucla.edu/programs/workshops/m
From playlist Machine Learning for Physics and the Physics of Learning 2019
Ruth Heller: Optimal control of false discovery criteria in the general two-group model
CIRM VIRTUAL EVENT Recorded during the meeting "Mathematical Methods of Modern Statistics 2" the June 03, 2020 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide mathematicians
From playlist Virtual Conference
A Hidden Pattern which All texts must obey | A novel approach to Zipf' s Law. #SoME2
Find the original paper here: https://arxiv.org/pdf/1901.00521.pdf Zipf' s Law is observed when words are inversely proportional to their frequency in a given text. Heap' s Law predicts how many unique words we expect to find in a random text sample. Neither of these laws are perfect beca
From playlist Something you did not know...
Michael I. Jordan: Machine Learning: Dynamical, Stochastic & Economic Perspectives
2019 Purdue Engineering Distinguished Lecture Series presenter Dr. Michael I. Jordan While there has been significant progress at the interface of statistics and computer science in recent years, many fundamental challenges remain. Some are mathematical and algorithmic in nature, such as
From playlist AI talks
Systems Approaches for Combinatorial Biology - S. Chandrasekaran - 1/14/16
Bioinformatics Research Symposium Beckman Institute Auditorium Thursday, January 14, 2016
From playlist Bioinformatics Research Symposium
From playlist Hierarchical Clustering