General audiences presentation from the May Knime Meetup in Boston

1. Basic Probability 2. Introduction Probability is the study of randomness and uncertainty.In the early days, probability was associated withgames of chance(gambling). 3.…

1. Machine LearningCentral Problem of Pattern Recognition: Supervised and Unsupervised Learning Classification Bayesian Decision TheoryPerceptrons and SVMsClusteringVisual…

1. Artificial Neural Networks - MLP Artificial Intelligence Department of Industrial Engineering and Management Cheng Shiu University 2. Outline  Logic problem for linear…

1. SupportVectorMachinesMuhammadAdil RajaIntroductionIntuitionBehindMarginsNotationFunctionalandGeometricMarginsThe OptimalMarginClassifiersOptimalMarginClassifiersKernelsRegularizationand…

1. PATTERN RECOGNITION Talal A. Alsubaie SFDA 2. OUTLINES What is a pattern? What isApatternClass ? What is pattern recognition? Human Perception Examples of applications…

1. Seminar in Advanced Machine Learning Rong Jin 2. Course Description Introduction to the state-of-the-art techniques in machine learning Focus of this semester Convex optimization…

1. Machine Learning Techniquesfor HEP Data Analysis withT MVA Andreas H oecker ( * )(CERN)Seminar, LPNHEParis , June 20, 2007( * )On behalf of the author team: A. Hoecker,…

Slide 11 / 43 Freiburg Seminar, May 9, 2007A. Hoecker: Data Mining with TMVA HEP Data Mining with TMVA ToolKit for Multivariate Analysis with ROOT Andreas Hoecker ( * ) (CERN)…