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Machine Learning: Some applications Mrinal K. Sen Seismic+wells+horizons Inversion Pseudo logs AI,SI,ρ Well logs+core data porosity, saturation,…
Parametric Estimation X = { xt }t where xt ~ p x Parametric estimation: Assume a form for p x q and estimate q , its sufficient statistics, using X e.g., N μ, σ2…
A field guide to the machine learning zoo Theodore Vasiloudis SICSKTH From idea to objective function Formulating an ML problem Formulating an ML problem ● Common aspects…
Machine Teaching for Personalized Education, Security, Interactive Machine Learning Jerry Zhu NIPS 2015 Workshop on Machine Learning from and for Adaptive User Technologies…
Online Learning via Stochastic Optimization, Perceptron, and Intro to SVMs Piyush Rai Machine Learning CS771A Aug 20, 2016 Machine Learning CS771A Online Learning via Stochastic…
Lecture 31: Machine learning II CS221 Summer 2019 Jia Framework Dtrain Learner x f y Learner Optimization problem Optimization algorithm CS221 Summer 2019 Jia 1 Review w…
Machine Learning Seminar: Support Vector Regression Presented by: Heng Ji 10/08/03 Outline Regression Background Linear ε- Insensitive Loss Algorithm Primal Formulation…
Kernels for kernel-based machine learning Matthias Rupp Berlin Institute of Technology, Germany Institute of Pure and Applied Mathematics Navigating Chemical Compound Space…
Riemannian Geometry and Statistical Machine Learning Doctoral Thesis Guy Lebanon Language Technologies Institute School of Computer Science Carnegie Mellon University lebanon@cscmuedu…
CPSC 340: Machine Learning and Data Mining Regularization Fall 2015 Admin • No tutorialsclass Monday holiday. Radial Basis Functions • Alternative to polynomial bases…
Understanding and Visualizing Data Iteration in Machine Learning Fred Hohmanγ∗, Kanit Wongsuphasawatα, Mary Beth Keryχ, Kayur Patelα γ Georgia Tech Atlanta, GA, USA…
Journal of Machine Learning Research 10 (2009) 2741-2775 Submitted 2/09; Revised 7/09; Published 12/09 Reproducing Kernel Banach Spaces for Machine Learning Haizhang Zhang…
SIBGRAPI 2016 – TUTORIAL Image Operator Learning and Applications Igor S. Montagner Nina S. T. Hirata Roberto Hirata Jr. Department of Computer Science Institute of Mathematics…
Probabilistic Models Value-at-Risk (VaR) Chance constrained programming Min variance Max return s.t. Prob{function≥target}≥α Max Prob{function≥target} Max VaR Finland…
Probabilistic Parsing CSPP 56553 Artificial Intelligence February 25, 2004 Roadmap Probabilistic CFGs Handling ambiguity – more likely analyses Adding probabilities Grammar…
Quantum Mechanical Properties of Atoms in Molecules via Machine Learning Matthias Rupp Fritz Haber Institute of the Max Planck Society, Berlin, Germany Joint work with Raghunathan…
Reinforcement Learning George Konidaris [email protected] Spring 2016 mailto:[email protected] Machine Learning Subfield of AI concerned with learning from data. ! ! Broadly,…
Structured Reguarization Non-Smooth Optimization Wrap-Up CPSC 540: Machine Learning Structured Regularization Mark Schmidt University of British Columbia Winter 2018 Structured…
Data Mining and Machine Learning Madhavan Mukund Lecture 18, Jan–Apr 2020 https:www.cmi.ac.in~madhavancoursesdmml2020jan https:www.cmi.ac.in~madhavancoursesdmml2020jan…
CSC411: Optimization for Machine Learning University of Toronto September 20–26, 2018 1 1based on slides by Eleni Triantafillou, Ladislav Rampasek, Jake Snell, Kevin Swersky,…