On Machine Learning from A free book on ML – A First Encounter of Machine Learning by Max Welling
The first reason for the recent successes of machine learning and the growth of the field as a whole is rooted in its multidisciplinary character. Machine learning emerged from AI but quickly incorporated ideas from fields as diverse as statistics, probability, computer science, information theory, convex optimization, control theory, cognitive science, theoretical neuroscience, physics and more.
The second, perhaps more important reason for the growth of m
achine learning is the exponential growth of both available data and computer power. While the field is build on theory and tools developed statistics machine learning recognizes that the most exiting progress can be made to leverage the enormous flood of data that is generated each year by satellites, sky observatories, particle accelerators, the human genome project, banks, the stock market, the army, seismic measurements, the internet, video, scanned text and so on.
On why this book was written
Much of machine learning is built upon concepts from mathematics such as partial derivatives, eigenvalue decompositions, multivariate probability densities and so on. I quickly found that these concepts could not be taken for granted at an undergraduate level.
Machine learning will be one of the most important tech trends over the next three to five years for innovation” http://t.co/kBFPHlANHa
Startups making machine learning an elementary affair http://t.co/FkF7TSy45R
Use Cases Machine Learning on Big Data for Predictive Analytics http://t.co/1AvQHXkgr4 #ml usecases
A startup journey, the improvement in Python’s data science capabilities and hosted machine learning http://t.co/Vx4g7lIM1X #techtrends
RT @woycheck: Zico Kolter wants to use machine learning to analyze electrical current behavior and provide details about your power bill (@…
Microsoft Research Machine Learning Summit: April 22-24, 2013 http://t.co/x9YxylgMeX
RT @siah: A free ebook by Max Welling “A First Encounter with Machine Learning” http://t.co/5KjCCylL3Y
Google Hires Brains that Helped Supercharge Machine Learning | Wired Enterprise | http://t.co/cVgZpNri4c http://t.co/2mJ7ggZE2n
RT @siah: PyMADlib: A Python wrapper for MADlib – an open source library for scalable in-database machine learning algorithms http://t.c …
Peekaboo: Machine Learning Cheat Sheet (for scikit-learn) http://t.co/6UyYWO74
Panels and Discussions
This is a panel from Churchill Club featuring
Peter Norvig, Director of Research, Google ,Gurjeet Singh, Co-founder & CEO, Ayasdi, Jeremy Howard, President and Chief Scientist, Kaggle
Once in a while, I go and gather my recent tweets and create a Tweet Cloud (a project developed by a student). I find some interesting topics, save the tweets and start a blog. I have written about this Linked Tweet Cloud a couple of times.