Successful candidates of the MSc “Statistics in Smart Data” degree will have a solid background in the following statistical, mathematical and IT subjects: Programing, Algorithms and Data Structures   

Source URL: www.ensai.fr

Vector Derivatives, Gradients, and Generalized Gradient Descent Algorithms ECE 275A – Statistical Parameter Estimation Ken Kreutz-Delgado ECE Department, UC San Diego

Source URL: dsp.ucsd.edu

Algorithms for Optimal Power Allocation of Wireless Multi-Hop Heterogeneous Networks under Statistical Delay Constraints Neda Petreska (joint work with J. Gross and H. Al-Zubaidy) Fraunhofer Institute for Embedded System

Source URL: disco.cs.uni-kl.de

Evaluating the Quality of Online Optimization Algorithms by Discrete Event Simulation 1 A Online Appendix: Numerical Results This section contains a detailed statistical summary of the numerical results gathered during

Source URL: dol.ior.kit.edu

Monte Carlo Methods in Statistical Mechanics: Foundations and New Algorithms Alan D. Sokal Department of Physics New York University

Source URL: www.stat.unc.edu

Automatic Derivation of Statistical Algorithms: The EM Family and Beyond Alexander G. Gray Carnegie Mellon University

Source URL: www.fast-lab.org

With the advent of massive data sets, statistical learning and information processing techniques are expected to enable unprecedented possibilities for better decision making. However, existing algorithms for mathematica

Source URL: mmds-data.org

On the Statistical Consistency of Algorithms for Binary Classification under Class Imbalance Aditya Krishna Menon University of California, San Diego, La Jolla CA 92093, USA

Source URL: jmlr.org

That was fast! Speeding up NN search of high dimensional distributions. Emanuele Coviello University of California, San Diego, 9500 Gilman Dr, La Jolla, CA 92093

Source URL: eceweb.ucsd.edu

• Statistics
• Geometry
• Statistical distance
• Information
• Statistical theory
• Information theory
• Bregman divergence
• Search algorithms
• KullbackLeibler divergence
• Variational Bayesian methods
• Expectationmaximization algorithm
• Divergence

MEMOCODE 2014 Design Contest: k-Nearest Neighbors with Mahalanobis Distance Metric Peter Milder Department of Electrical and Computer Engineering Stony Brook University Stony Brook, NY 11794–2350

Source URL: www.ece.stonybrook.edu

• Statistics
• Data analysis
• Computing
• Search algorithms
• Covariance and correlation
• Multivariate statistics
• Machine learning
• Mahalanobis distance
• Statistical distance
• Xeon
• Nearest neighbor search
• K-nearest neighbors algorithm