A Scalable Hierarchical Clustering Algorithm Using Spark: Spark Summit East talk by Chen Jin
Clustering is often an essential first step in datamining intended to reduce redundancy, or define data categories. Hierarchical clustering, a widely used ...
Spark Summit
Idea to Algorithm: The Full Workflow Behind Developing a Quantitative Trading Strategy
The process of strategy development is that of turning ideas into money. There are numerous steps in between, many of which are unknown to people entering ...
Quantopian
Stanley Osher: "Linearized Bregman Algorithm for L1-regularized Logistic Regression"
Graduate Summer School 2012: Deep Learning, Feature Learning "Linearized Bregman Algorithm for L1-regularized Logistic Regression" Stanley Osher, UCLA ...
Institute for Pure & Applied Mathematics (IPAM)
Privacy-preserving algorithms for decentralised collaborative learning: Dr Aurélien Bellet
Short bio I am a tenured researcher at Inria, where I am part of the Magnet Team (MAchine learninG in information NETworks). I am also affiliated with CRIStAL ...
The Alan Turing Institute
Lecture 22 - Clustering Part 3 Algorithms and Evaluation
In this lecture Dr. Neil Clark describes basic concept of unsupervised data clustering.
Avi Ma'ayan
Basic Algorithms in Message Passing System
This lecture covers the following topics: Basic Message Passing Model Types of Message Passing Systems- (i) Asynchronous and (ii) Synchronous systems ...
Distributed Systems
Belief Propagation Algorithms for Crowdsourcing
Crowdsourcing on platforms like Amazon's Mechanical Turk have become a popular paradigm for labeling large datasets. However, it has given rise to the ...
Microsoft Research
Improve Machine Learning Predictions using Graph Algorithms
Graph enhancements to AI and ML are changing the landscape of intelligent applications. In this webinar, we'll focus on using graph feature engineering to ...
Neo4j
The Science and Application of Data Compression Algorithms
Data compression is a ubiquitous aspect of modern computing, but not necessarily well-understood or optimally implemented. This talk will cover fundamental ...
CernerEng
K-Means Clustering Algorithm - Cluster Analysis | Machine Learning Algorithm | Data Science |Edureka
Data Science Training - https://www.edureka.co/data-science-r-programming-certification-course ) This Edureka k-means clustering algorithm tutorial video ...
edureka!
Stanford Seminar - WeBuildAI: Participatory framework for algorithmic governance
Min Kyung Lee Carnegie Mellon University January 18, 2019 Algorithms increasingly govern societal functions, impacting multiple stakeholders and social ...
stanfordonline
Lars Ruthotto: Distributed algorithms for full-waveform-inversion
Visit http://julialang.org/ to download Julia.
The Julia Programming Language
Seminario | Towards Principled Algorithms For Stochastic Optimal Control ... - Riccardo Bonalli
Seminario | Towards Principled Algorithms For Stochastic Optimal Control Of Nonlinear Mechanical Systems.
PoliTo Teaching
[DeepBayes2019]: Day 1, Lecture 4. Latent variable models and EM-algorithm
Slides: https://github.com/bayesgroup/deepbayes-2019/blob/master/lectures/day1/3.%20Dmitry%20Vetrov%20-%20Latent%20variable%20models.pdf Lecturer: ...
BayesGroup.ru
Fast Flow Algorithms via Cut-Approximators
Jonah Sherman, UC Berkeley Fast Algorithms via Spectral Methods http://simons.berkeley.edu/talks/jonah-sherman-2014-12-05.
Simons Institute
14.475 External Merge Sort
My book "Patterns in Data Management" is now available both as an ebook or a print book (with color graphics!). See: http://amzn.to/1Ts3rwx This book is not a ...
Prof. Dr. Jens Dittrich
Optimal Join Algorithms meet Top-k: Part 1 (SIGMOD 2020 tutorial)
Top-k queries have been studied intensively in the database community and they are an important means to reduce query cost when only the “best” or “most ...
DATA Lab Northeastern
Transfer Learning: Repurposing ML Algorithms from Different Domains to Cloud Defense
Mark Russinovich, Chief Technology Officer, Azure, Microsoft Machine learning algorithms are key to modern at-scale cyber-defense. Transfer learning is a state ...
RSA Conference
Stochastic Optimization and Sparse Statistical Recovery: An Optimal Algorithm for High Dimensions
We develop and analyze stochastic optimization algorithms for problems in which the expected loss is strongly convex, and the optimum is (approximately) ...
Microsoft Research
Machine Learning Day 2013 - Clustering; Geometry Preserving Non-Linear Dimension Reduction
Clustering: Probably Approximately Useless?, Rich Caruana (MSR) Clustering never seems to live up to the hype. To paraphrase the popular saying, clustering ...
Microsoft Research
Compiler Optimizations for Graph Algorithms on GPUs
Graphics Processing Units (GPUs) are an attractive target for graph algorithms because they support massively parallel execution and possess much higher ...
Microsoft Research
Graph Data Science with Neo4j Graph Algorithms - Will Lyon
Graph algorithms provide one of the most potent approaches to analysing connected data. They describe steps to be taken to pGraph algorithms provide one of ...
Neo4j
Lessons Learned while Implementing a Sparse Logistic Regression Algorithm in Spark with Lorand Dali
"This talk tells the story of implementation and optimization of a sparse logistic regression algorithm in spark. I would like to share the lessons I learned and the ...
Databricks
Stanford Seminar - Learning and Predictions in Autonomous Systems
Francesco Borrelli UC Berkeley October 25, 2019 Forecasts play an important role in autonomous and automated systems. Applications include transportation ...
stanfordonline
[DeepBayes2018]: Day 1, lecture 3. Models with latent variables and EM-algorithm
Speaker: Dmitry Vetrov.
BayesGroup.ru
Iterative methods for sparse linear systems on GPU (3)
Lecture 3 by Dr Nathan Bell, at the Pan-American Advanced Studies Institute (PASI)—"Scientific Computing in the Americas: the challenge of massive ...
Boston University
Advances in Cloud-Scale Machine Learning for Cyber-Defense
Mark Russinovich, Chief Technology Officer, Microsoft Azure, Microsoft Learn the latest frameworks, techniques and the unconventional machine-learning ...
RSA Conference
Fred Chong: Closing the Gap between Quantum Algorithms and Machines with Hardware-Software Co-Design
Quantum computing is at an inflection point, where 79-qubit (quantum bit) machines are being tested, 100-qubit machines are just around the corner, and even ...
QuICS
Protecting People from Algorithms (and Vice Versa)
Computing technologies today have made it much easier to gather personal data. Algorithms are constantly analyzing such personal information and making ...
Microsoft Research
Improve ML Predictions using Graph Algorithms
Graph enhancements to AI and ML are changing the landscape of intelligent applications. In this session, we'll focus on how using connected features can help ...
Neo4j
FLOW Seminar: Sashank Reddi (Google) Adaptive Federated Optimization
Federated Learning One World Seminar, 15th July 2020 Seminar: https://sites.google.com/view/one-world-seminar-series-flow/home Talk: ...
Federated Learning One World Seminar
PyData Tel Aviv Meetup: Intro to Data science workshop - Ido Zehori and Ishay Telavivi
PyData Tel Aviv Meetup - Intro to Data science workshop 16 December 2019 Sponsored and Hosted by ZeitGold https://www.meetup.com/PyData-Tel-Aviv/ This ...
PyData
Translation by Iterative Collaboration between Monolingual U
Google Tech Talk September 24, 2009 ABSTRACT Translation by Iterative Collaboration between Monolingual Users. Presented by Benjamin Bederson.
GoogleTechTalks
AWS re:Invent 2019: Paths to anomaly detection w/ TIBCO data science, streaming on AWS (AIM201-S)
Sensor data on the event stream can be voluminous. In NAND manufacturing, there are millions of columns of data that represent many measured and virtual ...
AWS Events
Algorithmic Inclusion: A Scalable Approach to Reducing Gender Bias in Google Translate | AISC
For slides and more information on the paper, visit ...
ML Explained - A.I. Socratic Circles - AISC
NIPS 2011 Big Learning - Algorithms, Systems, & Tools Workshop: Hazy - Making Data-driven...
Big Learning Workshop: Algorithms, Systems, and Tools for Learning at Scale at NIPS 2011 Invited Talk: Hazy: Making Data-driven Statistical Applications ...
GoogleTechTalks
Episode 12 - Divide and Conquer on Trees
This week's episode will cover the divide and conquer technique on trees. This technique is a precursor for understanding the centroid decomposition trees data ...
Algorithms Live!
Machine Learning Crash Course-2 Hours | Learn Machine Learning | Machine Learning Tutorial | Edureka
Machine Learning Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training Topics Wise Machine Learning Podcast ...
edureka!
Distributed Machine Learning Algorithms: Communication-Computation Trade-offs - Part 1
Distributed machine learning is an important area that has been receiving considerable attention from academic and industrial communities, as data is growing ...
Microsoft Research
Sketching Streaming Data: Efficient Collection & Processing | Lectures On-Demand
Professor Anna Gilbert, Department of Mathematics - University of Michigan Data Mining- The 4th University of Michigan Data Mining Workshop Sponsored by ...
University of Michigan Engineering
Penalty method for semidefinite programming and homework on linear matrix approximation
Lecture course 236330, Introduction to Optimization, by Michael Zibulevsky, Technion Penalty method for semidefinite programming and homework on linear ...
Technion
Introduction to Clustering
We will look at the fundamental concept of clustering, different types of clustering methods and the weaknesses. Clustering is an unsupervised learning ...
Data Science Dojo
