Not surprisingly, interest in this opportunity went through the roof. With only 500 seats initially available, and applications already covering that number multiple times, the competition to reserve a spot in the workforce of the future, especially given the fact that graduates will be given preference by Nanodegree including Didi Chuxing and IBM Watson, is guaranteed to be fierce. For graduates of the 2011 AI class, Introduction to Artificial Intelligence, the fact that the instructor line up reunites Sebastian Thrun and Peter Norvig is another incentive to enrol.

All the core content from the text rewritten in bulletized form for cut-to-the-chase mastery of the subject. Includes all objective testable terms, concepts, persons, places and events in browser based e-book format. Not just the facts, but interactive problem solving labs to ensure you master the concepts as well. Lab tools allow for thread-like collaboration among classmates and friends. Includes pre-made flashcards, and practice tests in true or false, multiple choice, mastery, or completion formats.

One of the central themes in Sum-Product networks (SPNs) is the interpretation of sum nodes as marginalized latent variables (LVs). This interpretation yields an increased syntactic or semantic structure, allows the application of the EM algorithm and to efficiently perform MPE inference. In literature, the LV interpretation was justified by explicitly introducing the indicator variables corresponding to the LVs' states. However, as pointed out in this paper, this approach is in conflict with the completeness condition in SPNs and does not fully specify the probabilistic model. We propose a remedy for this problem by modifying the original approach for introducing the LVs, which we call SPN augmentation. We discuss conditional independencies in augmented SPNs, formally establish the probabilistic interpretation of the sum-weights and give an interpretation of augmented SPNs as Bayesian networks. Based on these results, we find a sound derivation of the EM algorithm for SPNs. Furthermore, the Viterbi-style algorithm for MPE proposed in literature was never proven to be correct. We show that this is indeed a correct algorithm, when applied to selective SPNs, and in particular when applied to augmented SPNs. Our theoretical results are confirmed in experiments on synthetic data and 103 real-world datasets.

Amid the wall-to-wall coverage of the U.S. presidential race, it was easy to miss the Obama administration's release this month of a slim, 48-page report titled "Preparing for the Future of Artificial Intelligence." Yet the subject of the report -- and the changes it foreshadows -- may prove to be as consequential for our society, and our education system, as even the most high-stakes national election. The term "artificial intelligence" means different things to different people, but broadly speaking, it refers to computers and advanced machines that can think, reason and communicate like humans, respond to novel or nuanced situations as a person might, and most critically, learn from experiences as a human would. According to a recent survey, 80 percent of AI researchers believe that computers and advanced machines will eventually achieve levels of artificial intelligence that rival human intelligence. Moreover, half believe that this will happen by the year 2040 -- just one generation from now.

Following are the White House's strategy recommendations for dealing with artificial intelligence: Strategy 1: Make long-term investments in AI research. Prioritize investments in the next generation of AI that will drive discovery and insight and enable the United States to remain a world leader in AI. less Following are the White House's strategy recommendations for dealing with artificial intelligence: Strategy 1: Make long-term investments in AI research. Strategy 2: Develop effective methods for human-AI collaboration. Rather than replace humans, most AI systems will collaborate with humans to achieve optimal performance. Research is needed to create effective interactions between humans and AI systems.

Want to watch this again later? Need to report the video? This feature is not available right now. If you want Open Course Video Playlist, welcome to: http://opencourseonline.com/playlist If you are interest on more free online course info, welcome to: http://opencourseonline.com/ Professor Dan Jurafsky & Chris Manning are offering a free online course on Natural Language Processing starting in March 19, 2012.

DAVID VOGT'S son loves Lego. As they played together one day, the robotics professor had an idea: could he teach a robot to put the blocks together? "We thought it would be funny to make a robot that could do the same thing I am doing with my son," says Vogt, who is at the Freiburg University of Mining and Technology in Germany. So Vogt and his colleagues brought an industrial robot arm to the lab. Like a child playing for the first time, the robot – equipped with a Kinect depth camera – observed two experienced humans wearing motion tracking tags as they built a Lego rocket.

Deep data science is a branch of data science that has little if any overlap with closely related fields such as machine learning, computer science, operations research, mathematics, or statistics. Even classical machine learning and statistical techniques such as clustering, density estimation, or tests of hypotheses, have model-free, data-driven, robust versions designed for automated processing (as in machine-to-machine communications), and thus these techniques also belong to deep data science. Note that unlike deep learning, deep data science is not the intersection of data science and artificial intelligence; however, the analogy between deep data science and deep learning is not completely meaningless, in the sense that both deal with automation. For a robust regression that will work even if all the traditional model assumptions are violated, click here. It is simple (it can be implemented in Excel and it is model-free), efficient and very comparable to the standard regression (when the model assumptions are not violated).

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. Check out this motivating and inspirational post, the author of which went from little understanding of machine learning to actively and effectively utilizing techniques in their job within a year.

We consider the problem of transfer learning in an online setting. Different tasks are presented sequentially and processed by a within-task algorithm. We propose a lifelong learning strategy which refines the underlying data representation used by the within-task algorithm, thereby transferring information from one task to the next. We show that when the within-task algorithm comes with some regret bound, our strategy inherits this good property. Our bounds are in expectation for a general loss function, and uniform for a convex loss. We discuss applications to dictionary learning and finite set of predictors. In the latter case, we improve previous $O(1/\sqrt{m})$ bounds to $O(1/m)$ where $m$ is the per task sample size.