A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings, and goes all the way to the frontiers of the subject such as infinite mixture models, GPs, and MCMC.

Continuing the mission of the past AGI conferences, AGI-16 gathers an international group of leading academic and industry researchers involved in scientific and engineering work aimed directly toward the goal of Artificial General Intelligence (AGI). AGI-16 @ New York will be held from July 16-19 of 2016, on the campus of the New School in Lower Manhattan. As a special event for 2016, the AGI-16 conference will be co-located with three other related conferences -- BICA-16, the Neural-Symbolic Workshop 2016 and the AI & Cognition Workshop 2016 -- as part of the overall Human-Level Intelligence 2016 (HLAI-16) event. AGI conferences are organized by the Artificial General Intelligence Society, in cooperation with the Association for the Advancement of Artificial Intelligence (AAAI). The proceedings of AGI-16 will be published as a book in Springer's Lecture Notes in AI series, and all the accepted papers will be available online.

I did a webcast earlier today about Bayesian statistics. Some time in the next week, the video should be available from O'Reilly. In the meantime, you can see my slides here: And here's a transcript of what I said: Thanks everyone for joining me for this webcast. At the bottom of this slide you can see the URL for my slides, so you can follow along at home. I'm Allen Downey and I'm a professor at Olin College, which is a new engineering college right outside Boston. Our mission is to fix engineering education, and one of the ways I'm working on that is by teaching Bayesian statistics. Bayesian methods have been the victim of a 200 year smear campaign. If you are interested in the history and the people involved, I recommend this book, The Theory That Would Not Die.

Data is rarely clean and often you can have corrupt or missing values. It is important to identify, mark and handle missing data when developing machine learning models in order to get the very best performance. In this post you will discover how to handle missing values in your machine learning data using Weka. How To Handle Missing Data For Machine Learning in Weka Photo by Peter Sitte, some rights reserved. The problem used for this example is the Pima Indians onset of diabetes dataset.

Deep learning is a fascinating field of study and the techniques are achieving world class results in a range of challenging machine learning problems. Which library should you use and which techniques should you focus on? In this post you will discover a 14-part crash course into deep learning in Python with the easy to use and powerful Keras library. This mini-course is intended for python machine learning practitioners that are already comfortable with scikit-learn on the SciPy ecosystem for machine learning. Applied Deep Learning in Python Mini-Course Photo by darkday, some rights reserved. Before we get started, let's make sure you are in the right place.

This has been one of the most intriguing questions in science fiction and philosophy since the advent of machines. With modern technology, such questions are no longer bound to creative conjecture. Machine learning is all around us. From deciding which movie you might want to watch next on Netflix to predicting stock market trends, machine learning has a profound impact on how data is understood in the modern era. This tutorial aims to give you an accessible introduction on how to use machine learning techniques for your projects and data sets. In just 20 minutes, you will learn how to use Python to apply different machine learning techniques -- from decision trees to deep neural networks -- to a sample data set.

Because we'll need something tasty to swill when our robot overlords finally come into their full artificial intelligence, a company in the UK is attempting to figure out if robots can help humans brew a better beer. While there won't be robots stirring batches of wort or sorting hops, artificial intelligence will play a big part in London-based firm IntelligentX's plan to brew beer, CNET reports. Here's how it'd work: consumers would try one of the company's four beers -- Amber AI, Black AI, Golden AI and Pale AI ---- and then weigh in via Facebook chat bot on the experience. That feedback will be fed to an algorithm called Automated Brewing Intelligence, or ABI, which will use the information to make changes to the next batch. Reinforcement learning and a process called bayesian decision making will teach the AI about the brewing experience.

If free books on machine learning aren't enough for you, Open Source Society University has a free online course on Data Science. I wonder how their football team will do this Fall?

Celestion loudspeakers have powered the performances of many noted guitar and bass players, including legends such as Jimi Hendrix. Deciding whether a loudspeaker is good enough for professional musicians is a lengthy and painstaking process. Each speaker has its own unique sound based on a combination of sonic characteristics, such as midrange character and brightness. Evaluating a musical instrument loudspeaker involves subjective judgement about whether it generates a "good" sound. Only engineers with years of experience can reliably make that decision, and then only after repeated listening to a single loudspeaker and comparing the sounds it produces with those produced by a reference speaker.

Personalizing the News Feed: A Large-Scale Recommendation Problem Personalization is a key component in ensuring user satisfaction, and at Yahoo, personalization is at the heart of several user-facing products. This talk will focus on how Yahoo built one of the largest news recommendation engines in the world: the Yahoo stream, which personalizes the news feed for several hundreds of millions of users on millions of content items. Beyond the scale, the success of the news feed also depends on whether it is able to engage the user long term. In this session, Yahoo's director of research will present the challenges and issues in designing an engaging stream, and attendees will also learn how to cope with sparsity of explicit feedback, how user behavior changes with context of the device, how to build machine learned models for each user, and the metric that allows Yahoo to optimize for long term user-engagement.