Broadly speaking, educational activities can be split into two categories: life skills and professional skills. The life skills that we all need to learn, and the way we learn them, have remained relatively consistent across the ages: how to communicate, socialize and survive. But you can argue that today's education system is skewed toward the second category, the teaching of professional skills and it's this category that will face the greatest opportunities and challenges over the next 50 years. While educators prepare students for lives of learning, it's more true to say their role is to prepare students for lifelong careers. But while that was a relatively simple task in the past, it's now much more difficult.

Everyone who gets going in Machine Learning (and Deep Learning) gets overwhelmed by the plethora of MOOCs available. Here, I try to give a comprehensive survey of such courses available freely on the internet. You can take this post as an complementary to this and this previous posts. I will try to highlight some important pointers such as the difficulty of the courses, the correct order in which these should to be completed, the right audience for these courses. You will get a feel of how these courses give you a stack of skills in your arsenal and how you can use them to develop practical machine learning systems.

SEBASTOPOL, CA–(Marketwired – July 07, 2016) – The inaugural O'Reilly Artificial Intelligence Conference explores the real-world opportunities of applied AI on September 26 and 27, 2016, in New York City. O'Reilly Media founder and CEO Tim O'Reilly says that the explosion of intelligent software has just begun. Companies and developers working on applied AI require a different kind of knowledge than the research presented by existing academic conferences. The O'Reilly AI Conference fills that need with deeply practical sessions on AI today -- how to implement and interact with AI, use cases, and best practices -- as well as inquiries into the future of intelligence engineering. Peter Norvig and Tim O'Reilly serve as honorary program chairs for the first O'Reilly AI conference, with Ben Lorica and Roger Chen as program chairs.

The entire AI system learns your environment and optimizes performance in real-time. The Expert System makes decisions regarding which parameters to tune based on patterns fed by the learning module. The Expert System also creates Client Behavior Profiles and uses fingerprinting to tailor Device-to-Access Point interactions.

The SaaS Co. is a Berlin-made company that scales and executes sales for B2B SaaS products with the help of deep learning. At TSC we find, contact, qualify, and set appointments with decision makers in order to hand them over to you. In this lecture-workshop-breakfast, you will learn how to win as a customer, enterprises like Microsoft, or startups like Twilio. On the other hand we will show you how to use Artificial Intelligence in a practical way in your daily sales processes.

Machine learning is the science of discovering statistical dependencies in data, and the use of those dependencies to perform predictions. During the last decade, machine learning has made spectacular progress, surpassing human performance in complex tasks such as object recognition, car driving, and computer gaming. However, the central role of prediction in machine learning avoids progress towards general-purpose artificial intelligence. As one way forward, we argue that causal inference is a fundamental component of human intelligence, yet ignored by learning algorithms. Causal inference is the problem of uncovering the cause-effect relationships between the variables of a data generating system. Causal structures provide understanding about how these systems behave under changing, unseen environments. In turn, knowledge about these causal dynamics allows to answer "what if" questions, describing the potential responses of the system under hypothetical manipulations and interventions. Thus, understanding cause and effect is one step from machine learning towards machine reasoning and machine intelligence. But, currently available causal inference algorithms operate in specific regimes, and rely on assumptions that are difficult to verify in practice. This thesis advances the art of causal inference in three different ways. First, we develop a framework for the study of statistical dependence based on copulas and random features. Second, we build on this framework to interpret the problem of causal inference as the task of distribution classification, yielding a family of novel causal inference algorithms. Third, we discover causal structures in convolutional neural network features using our algorithms. The algorithms presented in this thesis are scalable, exhibit strong theoretical guarantees, and achieve state-of-the-art performance in a variety of real-world benchmarks.

Everyone who gets going in Machine Learning (and Deep Learning) gets overwhelmed by the plethora of MOOCs available. Here, I try to give a comprehensive survey of such courses available freely on the internet. You can take this post as an complementary to this and this previous posts. I will try to highlight some important pointers such as the difficulty of the courses, the correct order in which these should to be completed, the right audience for these courses. You will get a feel of how these courses give you a stack of skills in your arsenal and how you can use them to develop practical machine learning systems.

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.