Many real-world regression problems demand a measure of the uncertainty associated with each prediction. Standard decision forests deliver efficient state-of-the-art predictive performance, but high-quality uncertainty estimates are lacking. Gaussian processes (GPs) deliver uncertainty estimates, but scaling GPs to large-scale data sets comes at the cost of approximating the uncertainty estimates. We extend Mondrian forests, first proposed by Lakshminarayanan et al. (2014) for classification problems, to the large-scale non-parametric regression setting. Using a novel hierarchical Gaussian prior that dovetails with the Mondrian forest framework, we obtain principled uncertainty estimates, while still retaining the computational advantages of decision forests. Through a combination of illustrative examples, real-world large-scale datasets, and Bayesian optimization benchmarks, we demonstrate that Mondrian forests outperform approximate GPs on large-scale regression tasks and deliver better-calibrated uncertainty assessments than decision-forest-based methods.

So basically... I've spent the past several years of my life working in biology and biomedical engineering, but I've always felt very called by math and computer science. I find biology interesting, however... academic jobs are absurdly competitive, and in industry... because biology is so fickle, and the whole point of industry is naturally to make money, the level of difficulty of the problems that most biotech companies I see are facing are not fundamental research questions. So... I'm planning on starting a PhD program in Fall of 2017, and I have the opportunity to do a computational only PhD, that might be sort of a... machine learning-y data science-y kind of gig. But it would largely be implementation/analysis, probably no theory at all. I'm trying to get a feel for the extent that a mostly analysis/implementation PhD hinder my career goals after graduate schools.

Over half a million students fail to graduate from high school every year. In higher education, similar issues of retention arise, especially for STEM students. Experienced educators can pinpoint students at risk of failure, but the solution doesn't scale well, cannot be used to rank students with the highest risk, and is open to personal biases. Dr. Everaldo Aguiar's PhD research looked out how to use machine learning, based on large amounts of historical data collected by schools, to see if at risk students could be identified. In the recent Computer Science Seminar held May 19 at Northeastern University–Seattle, Dr. Aguiar presented the development, deployment and evaluation of machine learning models that detect, ahead of time, students at risk of underachieving their academic goals.

Will learner-centred AI be banned from classrooms like smartphones? I was struck by a statement in this promotional video for IBM's Watson AI technology that said, In the 30 or so years of working with digital platforms across the education and creative sectors I've noticed that these sort of claims appear every time a new bit of tech arrives. Watson, of course, is very smart technology. It hasn't passed the Turing test but it did beat the human champions on the TV trivia game show Jeopardy! It achieves this with some impressive computing power. Designed to answer questions within 3 seconds Watson's main innovation is its ability to quickly execute more than 100 different language analysis techniques to analyse the question, find and generate candidate answers, and ultimately score and rank them.

Everyone had his or her favorite drink in hand. There were bubbles and deep reds, and the sound of ice clinking in cocktail glasses underlay the hum of contented chatter. Gracing the room were slender women with long hair and men dressed in black suits, with glints of gold necklaces and cuff links. But it was no Gatsby affair. It was the annual Imperial College quantum gravity cocktail hour. The host was dressed down in black from head to toe--black turtleneck, jeans, and trench coat.

Should the government regulate artificial intelligence? That was the central question of the first White House workshop on the legal and governance implications of AI, held in Seattle on Tuesday. "We are observing issues around AI and machine learning popping up all over the government," said Ed Felten, White House deputy chief technology officer. "We are nowhere near the point of broadly regulating AI … but the challenge is how to ensure AI remains safe, controllable, and predictable as it gets smarter." One of the key aims of the workshop, said one of its organizers, University of Washington law professor Ryan Calo, was to help the public understand where the technology is now and where it's headed.

Good visual aids help me a ton. I'm just trying to get a foothold with ML with the end goal of making a balancing quad walker robot. I have a cs background.

We introduce a new representation learning approach for domain adaptation, in which data at training and test time come from similar but different distributions. Our approach is directly inspired by the theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made based on features that cannot discriminate between the training (source) and test (target) domains. The approach implements this idea in the context of neural network architectures that are trained on labeled data from the source domain and unlabeled data from the target domain (no labeled target-domain data is necessary). As the training progresses, the approach promotes the emergence of features that are (i) discriminative for the main learning task on the source domain and (ii) indiscriminate with respect to the shift between the domains. We show that this adaptation behaviour can be achieved in almost any feed-forward model by augmenting it with few standard layers and a new gradient reversal layer. The resulting augmented architecture can be trained using standard backpropagation and stochastic gradient descent, and can thus be implemented with little effort using any of the deep learning packages. We demonstrate the success of our approach for two distinct classification problems (document sentiment analysis and image classification), where state-of-the-art domain adaptation performance on standard benchmarks is achieved.

The recent media frenzy about artificial intelligence (AI) has been unavoidable. This vision has perhaps come a step closer with the arrival of IBM Watsoni and Richard Susskind's latest book, The Future of the Professionsii, which predicts an internet society with greater virtual interaction with professional services such as doctors, teachers, accountants, architects and lawyers. In reality, is AI many years away from making any real impact in the legal sector? And should law firms see this technical advancement as an opportunity or threat? Broadly speaking, AI is the theory and development of computer systems which will perform tasks that normally require human intelligence.

This course is the next logical step in my deep learning, data science, and machine learning series. I've done a lot of courses about deep learning, and I just released a course about unsupervised learning, where I talked about clustering and density estimation. So what do you get when you put these 2 together? In these course we'll start with some very basic stuff - principal components analysis (PCA), and a popular nonlinear dimensionality reduction technique known as t-SNE (t-distributed stochastic neighbor embedding). Next, we'll look at a special type of unsupervised neural network called the autoencoder.