Clustering Introduction - Practical Machine Learning Tutorial with Python p.34
In this tutorial, we shift gears and introduce the concept of clustering. Clustering is form of unsupervised machine learning, where the machine automatically determines the grouping for data. There are two major forms of clustering: Flat and Hierarchical. Flat clustering allows the scient
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Clustering (2): Hierarchical Agglomerative Clustering
Hierarchical agglomerative clustering, or linkage clustering. Procedure, complexity analysis, and cluster dissimilarity measures including single linkage, complete linkage, and others.
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From playlist Clustering Algorithms
We will look at the fundamental concept of clustering, different types of clustering methods and the weaknesses. Clustering is an unsupervised learning technique that consists of grouping data points and creating partitions based on similarity. The ultimate goal is to find groups of simila
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Hierarchical Clustering 5: summary
[http://bit.ly/s-link] Summary of the lecture.
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New Insight into Cosmology and the Galaxy-Halo Connection from Non-Linear Scales
Institute for Advanced Study / Princeton University Joint Astrophysics Colloquium Topic: New Insight into Cosmology and the Galaxy-Halo Connection from Non-Linear Scales Speaker: Frank van den Bosch Affiliation: Yale University Date: October 11, 2022 In our LCDM paradigm, galaxies form
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Towards an accurate cosmological measurements with optical clusters
Institute for Advanced Study Astrophysics Seminar Topic: Towards an accurate cosmological measurements with optical clusters Bloomberg Lecture Hall Speaker: Tomomi Sunayama Affiliation: The University of Arizona Department of Astronomy and Steward Observatory Date: October 13, 2022 Galax
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NIPS 2011 Domain Adaptation Workshop: Domain Adaptation with Multiple Latent Domains
Domain Adaptation Workshop: Theory and Application at NIPS 2011 Invited Talk: Domain Adaptation with Multiple Latent Domains by Kate Saenko Abstract: Domain adaptation is important for practical applications of supervised learning, as the distribution of inputs can differ significan
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Emulating Non-Linear Clustering - Jeremy Tinker
Jeremy Tinker - September 25, 2015 http://sns.ias.edu/~baldauf/Bias/index.html The interpretation of low-redshift galaxy surveys is more complicated than the interpretation of CMB temperature anisotropies. First, the matter distribution evolves nonlinearly at low redshift, limiting the u
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The Universe at Extreme Magnification by Jose Diego
Program Cosmology - The Next Decade ORGANIZERS : Rishi Khatri, Subha Majumdar and Aseem Paranjape DATE : 03 January 2019 to 25 January 2019 VENUE : Ramanujan Lecture Hall, ICTS Bangalore The great observational progress in cosmology has revealed some very intriguing puzzles, the most i
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Michael Lindsey - Quantum embedding with lower bounds - IPAM at UCLA
Recorded 28 March 2022. Michael Lindsey of the Courant Institute of Mathematical Sciences, Mathematics, presents "Quantum embedding with lower bounds" at IPAM's Multiscale Approaches in Quantum Mechanics Workshop. Abstract: We present quantum embedding theories based on relaxations of the
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Quantitative Constraints on Assembly Bias: An Open-Source Approach with Halotools - Andrew Hearin
Andrew Hearin - September 25, 2015 http://sns.ias.edu/~baldauf/Bias/index.html The interpretation of low-redshift galaxy surveys is more complicated than the interpretation of CMB temperature anisotropies. First, the matter distribution evolves nonlinearly at low redshift, limiting the u
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Patterns and Positions of Zeros of fractional quantum Hall wavefunctions by GJ Sreejith
DISCUSSION MEETING : GEOMETRIC PHASES IN OPTICS AND TOPOLOGICAL MATTER ORGANIZERS : Subhro Bhattacharjee, Joseph Samuel and Supurna Sinha DATE : 21 January 2020 to 24 January 2020 VENUE : Madhava Lecture Hall, ICTS, Bangalore This is a joint ICTS-RRI Discussion Meeting on the geometric
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Jamie Haddock - Hierarchical and neural nonnegative tensor factorizations - IPAM at UCLA
Recorded 02 December 2022. Jamie Haddock of Harvey Mudd College presents "Hierarchical and neural nonnegative tensor factorizations" at IPAM's Multi-Modal Imaging with Deep Learning and Modeling Workshop. Abstract: Nonnegative matrix factorization (NMF) has found many applications includin
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Introduction to Hierarchical Clustering with College Scorecard Data
Clustering is an unsupervised machine learning technique where data need not be labeled. The goal of clustering is to find like-items such as similar customers, similar products, or similar students, just to name a few. Popular clustering algorithms include K-means and hierarchical cluster
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Dark Matter (Lecture 1) by Katelin Schutz
PROGRAM PHYSICS OF THE EARLY UNIVERSE (HYBRID) ORGANIZERS: Robert Brandenberger (McGill University, Canada), Jerome Martin (IAP, France), Subodh Patil (Leiden University, Netherlands) and L. Sriramkumar (IIT - Madras, India) DATE: 03 January 2022 to 12 January 2022 VENUE: Online and Ra
From playlist Physics of the Early Universe - 2022