This module, along with module 2B introduces two key concepts in statistics/epidemiology, confounding and effect modification. A relation between an outcome and exposure of interested can be confounded if a another variable (or variables) is associated with both the outcome and the exposure. In such cases the crude outcome/exposure associate may over or under-estimate the association of interest. Confounding is an ever-present threat in non-randomized studies, but results of interest can be adjusted for potential confounders.

Journal of Parallel and Distributed Computing 104, 19-35.

With the advent of Big Data era, data reduction methods are highly demanded given its ability to simplify huge data, and ease complex learning processes. Concretely, algorithms that are able to filter relevant dimensions from a set of millions are of huge importance. Although effective, these techniques suffer from the "scalability" curse as well. In this work, we propose a distributed feature weighting algorithm, which is able to rank millions of features in parallel using large samples. This method, inspired by the well-known RELIEF algorithm, introduces a novel redundancy elimination measure that provides similar schemes to those based on entropy at a much lower cost. It also allows smooth scale up when more instances are demanded in feature estimations. Empirical tests performed on our method show its estimation ability in manifold huge sets --both in number of features and instances--, as well as its simplified runtime cost (specially, at the redundancy detection step).

Artificial intelligence is being harnessed by voice-controlled personal assistants, chatbot financial services, and even smart thermostats--now the University of Waterloo is applying algorithms to improve an age-old profession: bricklaying. Researchers at the university used AI software to study how masons position their body during bricklaying, revealing new insights into the safest poses and most productive way to work through machine learning. "The people in skilled trades learn or acquire a kind of physical wisdom that they can't even articulate," said Carl Haas in a statement. Hass is a professor of civil and environmental engineering at Waterloo. The study was published in Automation in Construction today and analyzed 21 masons with varying levels of expertise.

Stepwise regression analysis, a technique that helps you select the best subset of predictors for a regression analysis, when you have a big number of predictors. This way you can create regression models that are both parsimonious and effective. After finishing this course, you will be able to fit any nonlinear regression model using SPSS. K nearest neighbor, a very popular predictive technique used mostly for classification purposes. So you will learn how to predict the values of a categorical variable with this method.

Opaque and potentially biased mathematical models are remaking our lives--and neither the companies responsible for developing them nor the government is interested in addressing the problem. This week a group of researchers, together with the American Civil Liberties Union, launched an effort to identify and highlight algorithmic bias. The AI Now initiative was announced at an event held at MIT to discuss what many experts see as a growing challenge. Algorithmic bias is shaping up to be a major societal issue at a critical moment in the evolution of machine learning and AI. If the bias lurking inside the algorithms that make ever-more-important decisions goes unrecognized and unchecked, it could have serious negative consequences, especially for poorer communities and minorities. The eventual outcry might also stymie the progress of an incredibly useful technology (see "Inspecting Algorithms for Bias").

An autonomous robot will be deployed to an offshore oil and gas platform in the North Sea later this year, in a first for the sector. The £4m project's backers said the move was designed to take humans out of dangerous and dull jobs, and reinvent oil and gas as an industry of the future. Under the pilot scheme, the robot will initially be deployed at the French oil firm Total's gas plant on Shetland before being sent to join the 120 workers on the company's Alwyn platform, 440km north-east of Aberdeen. The machine, made by Austrian firm Taurob and supported on the software side by German university TU Darmstadt, will be used for visual inspections and detecting gas leaks. Rebecca Allison, asset integrity solution centre manager at the publicly-funded Oil and Gas Technology Centre, insisted autonomous robots would not be used to cut the wage burden of offshore workers who are paid a premium for working in tough, remote conditions.

Linear regression models are not perfect. It tries to approximate the relationship between dependent and independent variables in a straight line. Some errors can be reduced. Some errors are inherent in the nature of the problem. These errors cannot be eliminated. They are called as an irreducible error, the noise term in the true relationship that cannot fundamentally be reduced by any model.

"There is great potential in machine learning and other artificial intelligence methods to discover new insights. There are new findings showing that retina scans are an early predictor of heart disease, for example, and we never would have had that information before had we not been able to pool all this data together and bring artificial intelligence and machine learning to the table," Hall said.

It is hard to imagine a more scathing indictment of our ability to read another's thoughts and intentions than our inability to predict Donald Trump's next move. From the gross pre-election misjudgments to postelection bafflement, the best pundits are at a loss to accurately anticipate his response to matters like North Korean military aggressiveness or his moment-by-moment political gyrations and opinion reversals. Labeling Trump a narcissist, psychopath, megalomaniac or attention-impaired, or all of the above, might feel explanatory, but even when armed with the best psychoanalytic insights, we have no idea what he will do when presented with a new or unforeseen circumstance. If conventional psychology isn't up to the task, perhaps we should step back and consider a tantalizing sci-fi alternative -- that Trump doesn't operate within conventional human cognitive constraints, but rather is a new life form, a rudimentary artificial intelligence-based learning machine. When we strip away all moral, ethical and ideological considerations from his decisions and see them strictly in the light of machine learning, his behavior makes perfect sense. Consider how deep learning occurs in neural networks such as Google's Deep Mind or IBM's Deep Blue and Watson.