I will update this article regularly. An old version can be found here and has many interesting links. All the material presented here is not in the old version. This article is divided into 11 sections. A laptop is the ideal device.

Machine learning enables computers to learn from large amounts of data without being explicitly programmed to do so. We can already see how machine learning gives rise to new intelligent applications, from self-driving cars to intelligent assistants on our smartphones. Increasingly, businesses recognize the importance of using machine learning to transform their data assets into business value. However, many companies are unsure how machine learning can be applied to solve problems in an enterprise context. As the world's most relevant enterprise data is part of SAP's system and business network, SAP aspires to make all its enterprise solutions intelligent and help customers to leverage their data.

A tutorial given at NAACL HLT 2013. Machine learning is everywhere in today's NLP, but by and large machine learning amounts to numerical optimization of weights for human designed representations and features. The goal of deep learning is to explore how computers can take advantage of data to develop features and representations appropriate for complex interpretation tasks. This tutorial aims to cover the basic motivation, ideas, models and learning algorithms in deep learning for natural language processing. Recently, these methods have been shown to perform very well on various NLP tasks such as language modeling, POS tagging, named entity recognition, sentiment analysis and paraphrase detection, among others.

How will AI shape the average North American city by 2030? A panel of experts assembled as part of a century-long study into the impact of AI thinks its effects will be profound. The One Hundred Year Study on Artificial Intelligence is the brainchild of Eric Horvitz, a computer scientist, former president of the Association for the Advancement of Artificial Intelligence, and managing director of Microsoft Research's main Redmond lab. Every five years a panel of experts will assess the current state of AI and its future directions. The first panel, comprised of experts in AI, law, political science, policy, and economics, was launched last fall and decided to frame their report around the impact AI will have on the average American city.

I can't offer much in terms of other entry level recommendations, but I can recommend you learn to utilize the resource pages on the coursera course. The way the andrew NG course is set up is that you more or less try to have an idea of how these algorithms work at a conceptual level through the videos, then when you go to programming assignments, you can skip a lot of the prep work and focus on implementing the machine learning algorithms. Now those algorithms might be a little hard to follow at first, which is okay and expected, and that's where the lecture notes and/or wiki come in. From the wiki you can more or less translate the math formulas into code syntax and the assignments are more or less complete. The weeks build off each other so as you learn how to do one part, they do a little less prep work for you so you have to learn how to do another part, and so forth.

In May, Google research scientist Douglas Eck left his Silicon Valley office to spend a few days at Moogfest, a gathering for music, art, and technology enthusiasts deep in North Carolina's Smoky Mountains. Eck told the festival's music-savvy attendees about his team's new ideas about how to teach computers to help musicians write music--generate harmonies, create transitions in a song, and elaborate on a recurring theme. Someday, the machine could learn to write a song all on its own. Eck hadn't come to the festival--which was inspired by the legendary creator of the Moog synthesizer and peopled with musicians and electronic music nerds--simply to introduce his team's challenging project. To "learn" how to create art and music, he and his colleagues need users to feed the machines tons of data, using MIDI, a format more often associated with dinky video game sounds than with complex machine learning.

Note that, while there are numerous machine learning ebooks available for free online, including many which are very well-known, I have opted to move past these "regulars" and seek out lesser-known and more niche options for readers. Don't know where to start? If you are looking for something more, you could look here for an overview of MOOCs and online lectures from freely-available university lectures. Of course, nothing substitutes rigorous formal education, but let's say that isn't in the cards for whatever reason. Not all machine learning positions require a PhD; it really depends where on the machine learning spectrum one wants to fit in.

When it comes to robots replacing humans, we might think we have the upper hand since we're the ones who build and program them but that's not neccesarily the case anymore. Google is taking a different approach to training its robots – it's letting them teach each other. Researchers at Google have released a report showing how they connected 14 robotic arms together and used convolutional neural networks to let them teach themselves how to pick things up. The approach mimics how young children learn between the ages of one and four years old, and is essentially helping the robots to develop reliable hand-eye coordination. Typically, a robot would be programmed to carry out specific tasks, but this method shows how they can learn through trial-and-error in combination with a neural network – the same way a child learns how to do something by watching other people.

We are happy to announce that we can finally answer the question on how to cite mlr properly in publications. Our paper on mlr has been published in the open-access Journal of Machine Learning Research (JMLR) and can be downloaded on the journal home page. The paper gives a brief overview of the features of mlr and also includes a comparison with similar toolkits. For an in-depth understanding we still recommend our excellent online mlr tutorial which is now also available as a PDF on arxiv.org Once mlr 2.10 hits CRAN you can retrieve the citation information from within R:

In order to protect the world's iconic marine wildlife including whales, sea turtles and sharks, we first have to understand their biology. However, this is often easier said than done. In this 45-minute interactive lesson, students will be taken around the world to learn about new and exciting ways that marine scientists are uncovering the lives of these elusive creatures. Track humpback whales as they feed in Alaska, and come along for the ride as video cameras are deployed on sea turtles in Western Australia. The lesson uses photos and videos from a variety of active research projects, begins with historical context about human impacts on marine wildlife populations and ends with a discussion of what students can do in their lives to help learn about and protect our oceans.