Adnan Masood, a Machine Learning Phd's musings on Life, Technology, Research & Development
Going for a little Benoit B. Mandelbrot recursion joke here with the title. Seth Juarez (github) recently spoke to Pasadena .NET user group on the topic of Practical Machine Learning using nuML. Seth is a wonderful speaker, educator and nuML is an excellent library to get started with machine learning in .NET. His explanations are very intuitive; even…
Last night's LA Machine Learning event on Mining Time Series Data w/ Sylvia Halasz of YP at OpenX Pasadena was quite interesting and well attended. Dr. Halasz spoke about Adaptive Ensemble Kalman Filter and her work on building n-gram correlation with the flu outbreaks. Some of the associated papers follow. The ngram chief complaint classifier: A novel…
Causality, Probability and Time by Dr. Samantha Kelinberg is a whirlwind yet original journey of the interdisciplinary study of probabilistic temporal logic and causal inference. Probabilistic causation is a fairly demanding area of study which studies the relationship between cause and effect using the tools of probability theory. Judea Pearl, in his seminal text "Causality:…
A #NoteToSelf style post regarding collection of bayesian network repositories including but not limited to bnet, net, bif, dsc and rda files. GeNIe and SMILE Network Repository http://genie.sis.pitt.edu/networks.html BNLearn http://www.bnlearn.com/bnrepository/ University of Hebrew Bayesian Network Repository http://www.cs.huji.ac.il/~galel/Repository/ DSL lab Network Repository http://genie.sis.pitt.edu/networks.html Aalborg University Repository http://www.cs.auc.dk/research/DSS/Misc/networks.html Norsys Bayes Net Library http://www.norsys.com/networklibrary.html Encog Project - Example…
As statistician Dennis Lindley famously said, "Inside every nonBayesian there is a Bayesian struggling to get out"; it would be safe to interpolate that Sharon McGrayne's interesting tale of trials and triumph of the Bayes Rule, or more accurately Bayes-Laplace-Price rule, is an excellent historical journey, which may help get your Bayesian out of the…
For most researchers in the ever growing fields of probabilistic graphical models, belief networks, causal influence and probabilistic inference, ACM Turing award winner Dr. Judea Pearl and his seminary papers on causality are well-known and acknowledged. Representation and determination of Causality, the relationship between an event (the cause) and a second event (the effect), where…
Recently attended Big Data Event @ Caltech. The topic was Big Data, Big Opportunities: Predicting the Future One Byte at a Time and the panel and speakers didn't disappoint. Following is the slidedeck and pictures from the event.
If you are scouring for an exploratory text in probabilistic reasoning, basic graph concepts, belief networks, graphical models, statistics for machine learning, learning inference, naïve Bayes, Markov models and machine learning concepts, look no further. Dr. Barber has done a praiseworthy job in describing key concepts in probabilistic modeling and probabilistic aspects of machine learning.…
S. Abe and T. Inoue. Fuzzy support vector machines for multiclass problems.In ESANN 2002 Proceedings, pages 113-118, 2002. R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD Conference, 1993. A. Alink, C. M. Schwiedrzik, A. Kohler, W. Singer, and L.…
An interesting beginners talk for machine learning enthusiasts. Ever tried to use a regular expression to parse an unstructured street address? This talk is an introduction to a few machine learning algorithms and some tips for integrating them where they make the most sense and will save you the most headaches. Hilary Mason - Machine…