Machine learning not only is big in Microsoft Research, but it is pervasive throughout all Microsoft products. So whenever you use a Microsoft product you’re using a system that’s been generated from machine learning. By leveraging insights from Office 365 and mapping the relationships between people, groups, files and conversations through machine learning, we can intelligently connect and surface the most relevant content using the Office Graph. Cortana, the new digital personal assistant powered by Bing that comes with Windows Phone 8.1, continually learns about its user and becomes increasingly personalized, with the goal of proactively carrying out the right tasks at the right time. If its user asks about outside temperatures every afternoon before leaving the office, Cortana will learn to offer that information without being asked. Cortana’s design philosophy is entrenched in state-of-the-art machine-learning and data-mining algorithms.
Here are some resources that I love if you are diving into the world of Machine Learning.
|Machine Learning is fun||If you ever wondered where to start learning about Machine Learning, this is a great place to start.|
|Machine Learning with F#||F# is ideally suited to machine learning because of its efficient execution, succinct style, data access capabilities and scalability. F# has been successfully used by some of the most advanced machine learning teams in the world, including several groups at Microsoft Research.|
|Infer.net||Infer.NET is a framework for running Bayesian inference in graphical models. You can use Infer.NET to solve many different kinds of machine learning problems, from standard problems like classification or clustering through to customised solutions to domain-specific problems. Infer.NET has been used in a wide variety of domains including information retrieval, bioinformatics, epidemiology, vision, and many others.|
|Basics of Machine Learning||A series of videos on the basics of Machine Learning. Includes Naive Bayes, decision trees, zero-frequency, missing data, ID3 algorithm, information gain, overfitting, confidence intervals, nearest-neighbour method, Parzen windows, K-D trees, K-means, scree plot, gaussian mixtures, EM algorithm, dimensionality reduction, principal components, eigen-faces, agglomerative clustering, single-link vs. complete link, lance-williams algorithm.|
|Andrew Ng’s free Machine Learning class on Coursera||Learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. The next session starts Jun 16 2014 and you can register via http://bit.ly/1kBgZnH|
|Download: A Course in Machine Learning||
CIML is a set of introductory materials that covers most major aspects of modern machine learning (supervised learning, unsupervised learning, large margin methods, probabilistic modeling, learning theory, etc.). It’s focus is on broad applications with a rigorous backbone. A subset can be used for an undergraduate course; a graduate course could probably cover the entire material and then some.
|scikit-learn||scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.|
|Learning from Data||
Machine Learning course – recorded at a live broadcast from Caltech taught by Feynman Prize winner Professor Yaser Abu-Mostafa
|The Machine Learning Salon||The Machine Learning Salon provides free information about Machine Learning and Artificial Intelligence.
Its aim is to develop the understanding of Machine Learning Theory and its applications by providing a first set of useful website links to students and/or developers.
|Book:Machine Learning – The Complete Guide||This is a Wikipedia book, a collection of Wikipedia articles that can be easily saved, rendered electronically, and ordered as a printed book.|
|Machine Learning Summer School Videos||Videos from The Machine Learning Summer School that took place at the Max Planck Institute for Intelligent Systems, from 25 August to 8 September 2013. http://mlss.tuebingen.mpg.de/|
|Data Science Stack Exchange||
Data Science Stack Exchange is a free question and answer site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field.