As for the next frontier for applied nonparametrics, I think that it's mainly "get real about real-world applications". Are the SVM and boosting machine learning while logistic regression is statistics, even though they're solving essentially the same optimization problems up to slightly different shapes in a loss function? I'll resist the temptation to turn this thread into a Lebron vs MJ debate. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. Note that latent Dirichlet allocation is a parametric Bayesian model in which the number of topics K is assumed known. I find that industry people are often looking to solve a range of other problems, often not involving "pattern recognition" problems of the kind I associate with neural networks. Of course, the "statistics community" was also not ever that well defined, and while ideas such as Kalman filters, HMMs and factor analysis originated outside of the "statistics community" narrowly defined, there were absorbed within statistics because they're clearly about inference. As with many phrases that cross over… Prof. Jordan is a member of the National Academy of Sciences, a member of the National Academy of Engineering and a member of the American Academy of Arts and Sciences. I'd invest in some of the human-intensive labeling processes that one sees in projects like FrameNet and (gasp) projects like Cyc. Although current deep learning research tends to claim to encompass NLP, I'm (1) much less convinced about the strength of the results, compared to the results in, say, vision; (2) much less convinced in the case of NLP than, say, vision, the way to go is to couple huge amounts of data with black-box learning architectures. John Paisley, Chong Wang, Dave Blei and I have developed something called the nested HDP in which documents aren't just vectors but they're multi-paths down trees of vectors. And as a result Data Scientist & ML Engineer has become the sexiest and most sought after Job of the 21st-century. (2) How can I get meaningful error bars or other measures of performance on all of the queries to my database? Good stuff, the marketeers are out of control these days, it's engineers like him that gotta keep it real. Lets not fool ourselves though by saying that Deep learning, or machine learning is some sort of super smart AI sentient bot, its far from it and really doesn't have any true intelligence behind it. Michael I. Jordan: Machine Learning, Recommender Systems, and … I've been collecting methods to accelerate training in PyTorch – here's what I've found so far. Layered architectures involving lots of linearity, some smooth nonlinearities, and stochastic gradient descent seem to be able to memorize huge numbers of patterns while interpolating smoothly (not oscillating) "between" the patterns; moreover, there seems to be an ability to discard irrelevant details, particularly if aided by weight- sharing in domains like vision where it's appropriate. You need to know what algorithms are available for a given problem, how they work, and how to get the most out of them. I view them as basic components that will continue to grow in value as people start to build more complex, pipeline-oriented architectures. My first and main reaction is that I'm totally happy that any area of machine learning (aka, statistical inference and decision-making; see my other post :-) is beginning to make impact on real-world problems. My first and main reaction is that I’m totally happy that any area of machine learning (aka, statistical inference and decision-making; see my other post :-) is beginning to make impact on real-world problems. And of course it has engendered new theoretical questions. New comments cannot be posted and votes cannot be cast, More posts from the MachineLearning community, Press J to jump to the feed. Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. The methods – roughly sorted from largest to smallest expected speed-up – are: Consider using a different learning rate schedule. He that saying statistical ML systems can somewhat solve a class of problems that are a small subset of what "AI" really is. Following Prof. Jordan’s talk, Ion Stoica, Professor at UC Berkeley and Director of RISELab, will present: “The Future of Computing is Distributed” The demands of modern workloads, such as machine learning, are growing much faster than the capabilities of a single-node computer. In particular, they play an increasingly important role in the design and analysis of machine learning algorithms. Jordan is one of the world’s most respected authorities on machine learning and an astute observer of the field. Intellectually I think that NLP is fascinating, allowing us to focus on highly-structured inference problems, on issues that go to the core of "what is thought" but remain eminently practical, and on a technology that surely would make the world a better place. Moreover, not only do I think that you should eventually read all of these books (or some similar list that reflects your own view of foundations), but I think that you should read all of them three times---the first time you barely understand, the second time you start to get it, and the third time it all seems obvious. Note that latent Dirichlet allocation is a tree. This feature selector is trained to maximize the mutual information between selected features and the response variable, where the conditional distribution of the response … ... //bit.ly/33rAlsBHappy 50th Birthday Michael Jordan!Relive the best plays of Michael Jordan... Want to learn how to dunk like MJ ? (5) How can I do diagnostics so that I don't roll out a system that's flawed or so that I can figure out that an existing system is now broken? That list was aimed at entering PhD students at Berkeley,who I assume are going to devote many decades of their lives to the field, and who want to get to the research frontier fairly quickly. In our conversation with Michael, we explore his career path, and how his influence … Eventually we will find ways to do these things for more general problems. Although I could possibly investigate such issues in the context of deep learning ideas, I generally find it a whole lot more transparent to investigate them in the context of simpler building blocks. Will this trend continue, or do you think there is hope for less data-hungry methods such as coresets, matrix sketching, random projections, and active learning? This seems like as good a place as any (apologies, though, for not responding directly to your question). Worthy thing to Consider, and would you add any new ones of Edinburgh '' ( particularly the need large! 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