Mon premier blog

Machine Learning: A Probabilistic Perspective Kevin P. Murphy
Publisher: MIT Press

See the papers Machine Learning for Medical Diagnosis: History, State of the Art, and Perspective and Artificial Neural Networks in Medical Diagnosis. The next two books cover the same area, but are written from a Bayesian perspective. Jun 26, 2013 - The aim of this special session is to obtain a good perspective into the current state of practice of Machine Learning to address various predictive problems. Sep 16, 2013 - In this paper we propose a probabilistic learning method for tracing the boundaries of the breast and the pectoral muscle. In fact, you can achieve perfect predictions when you just output the values you got for training (ok, if they are unambiguous) without any real learning taking place at all. Probability can be very counter-intuitive. Feb 17, 2014 - I'm a PostDoc in machine learning at TU Berlin and co-founder and chief data scientist at streamdrill (formerly TWIMPACT), a startup working on real-time event analysis for all kinds of applications. Mar 10, 2011 - The authors have written an enjoyable book---rigorous in the treatment of the mathematical background, but also enlivened by interesting and original historical and philosophical perspectives. Dec 26, 2010 - In the previous list, I thought it would be good to recommend some lighter texts as introductions to topics like probability theory and machine learning. From the texture perspective, some mammograms are noisy in their boundaries. -- Manfred Jaeger, Aalborg Universitet Keywords » Bayesian Networks - Data Mining - Density Estimation - Hybrid Random Fields - Intelligent Systems - Kernel Methods - Machine Learning - Markov Random Fields - Probabilistic Graphical Models. I'm also adding a reference for looking at probability from the Bayesian perspective. The intuition behind calculating the probability using support vector machines is that the probability of the feature vectors near the decision boundary will be close, and, actually, on the decision boundary, the probability is equal to 0.5. Based upon subsequent discussions and feedback, I've changed my view. Straight into the deep end is the way to to choose from the probability list, in order to build a base in probability theory.