W e consider distributed online learning protocols that con trol the exchange of information between local learners in a round-based learning scenario. The learning performance of such a protocol is intuitively optimal if app roximately the same loss is incurred as in a hypothetical serial setting. If a pro tocol accomplishes this, it is inherently impossible to achieve a strong communicati on bound at the same time. In the worst case, every input is essential for the lear ning performance, even for the serial setting, and thus needs to be exchanged betwee n the local learners. However, it is reasonable to demand a bound that scales well w ith the hardness of the serialized prediction problem, as measured by the los s received by a serial online learning algorithm. W e provide formal criteria base d on this intuition and show that they hold for a simplified version of a previously pu blished protocol.

Machine learning engineers--i.e., advanced programmers who develop artificial intelligence (AI) machines and systems that can learn and apply knowledge--are in high demand, as more companies adopt these technologies. These professionals perform sophisticated programming, and work with complex data sets and algorithms to train intelligent systems. While many fear that AI will soon replace jobs, at this phase in the technology's development, it is still creating positions like machine learning engineers, as companies need highly-skilled workers to develop and maintain a wide range of applications. To help those interested in the field better understand how to break into a career in machine learning, we compiled the most important details and resources. This guide on how to become a machine learning engineer will be updated on a regular basis.

Advanced, graduate-level book for data scientists who wish to move into deep learning.

This course dives into the basics of machine learning using an approachable, and well-known programming language, Python. In this course, we will be reviewing two main components: First, you will be learning about the purpose of Machine Learning and where it applies to the real world. Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms. In this course, you practice with real-life examples of Machine learning and see how it affects society in ways you may not have guessed! By just putting in a few hours a week for the next few weeks, this is what you'll get. 1) New skills to add to your resume, such as regression, classification, clustering, sci-kit learn and SciPy 2) New projects that you can add to your portfolio, including cancer detection, predicting economic trends, predicting customer churn, recommendation engines, and many more.

With the recent trend of applying machine learning in every aspect of human life, it is important to incorporate fairness into the core of the predictive algorithms. We address the problem of predicting the quality of public speeches while being fair with respect to sensitive attributes of the speakers, e.g. gender and race. We use the TED talks as an input repository of public speeches because it consists of speakers from a diverse community and has a wide outreach. Utilizing the theories of Causal Models, Counterfactual Fairness and state-of-the-art neural language models, we propose a mathematical framework for fair prediction of the public speaking quality. We employ grounded assumptions to construct a causal model capturing how different attributes affect public speaking quality. This causal model contributes in generating counterfactual data to train a fair predictive model. Our framework is general enough to utilize any assumption within the causal model. Experimental results show that while prediction accuracy is comparable to recent work on this dataset, our predictions are counterfactually fair with respect to a novel metric when compared to true data labels. The FairyTED setup not only allows organizers to make informed and diverse selection of speakers from the unobserved counterfactual possibilities but it also ensures that viewers and new users are not influenced by unfair and unbalanced ratings from arbitrary visitors to the www.ted.com website when deciding to view a talk.

Everybody knows (or should know) by now that machine learning (which is what most current artificial intelligence actually amounts to) is subject to bias. Last week, the New York Times had the idea of asking three prominent experts in the field to talk about the bias problem, in particular the ways that social bias can be reflected and amplified in dangerous ways by the technology to discriminate against, or otherwise damage, certain social groups. At first sight, the resulting article looked like a run-of-the-mill review of what has become a common topic – except for one thing: the three experts were all women. One, Daphne Koller, is a co-founder of the online education company Coursera; another, Olga Russakovsky, is a Princeton professor who is working to reduce bias in ImageNet, the data set that powered the current machine-learning boom; the third, Timnit Gebru, is a research scientist at Google in the company's ethical AI team. Reading the observations of these three women brought to the surface a thought that's been lurking at the back of my mind for years.

The College of Humanities and Social Sciences (CHSS) at HBKU aims to deliver innovative programs that meet educational needs in the fields of humanities and social sciences for Qatar and the region. The College of Humanities and Social Sciences (CHSS) at Hamad Bin Khalifa University (HBKU) invites applications for Open Rank positions in the field of Translation Studies. The successful candidate will have long-standing experience in the field of Intercultural and Literary Translation, or Machine Translation, Artificial Intelligence and/or Terminology, a dynamic and innovative research agenda, as evidenced through an internationally recognized, strong record of peer-reviewed publications. The candidate will work closely with other programs in the college, in particular the PhD Program in Humanities and Social Sciences, and with national, regional and international partners and stakeholders. The candidate will be expected to teach graduate courses at MA and PhD level, applying a range of methodologies for teaching and assessment, contribute to all levels of curriculum development in the area(s) of specialty including the development of the interdisciplinary PhD in Humanities and Social Sciences.

Everybody wants to do machine learning these days. Machine learning, data science, artificial intelligence, deep learning, neural network -- these have become some of the most used phrases in the tech space today. I'm not saying it's particularly bad, but it definitely gets scary for somebody who doesn't really know what all this means but wants to get into the rat race. When you think about it, from a software developer's point of view, these are just different types of software or applications you work on, but with more math involved. I know I'm oversimplifying what data science is, but for somebody who doesn't have a mathematics or statistics background, it is very difficult to understand the jargon initially.

In order for an e-commerce platform to maximize its revenue, it must recommend customers items they are most likely to purchase. However, the company often has business constraints on these items, such as the number of each item in stock. In this work, our goal is to recommend items to users as they arrive on a webpage sequentially, in an online manner, in order to maximize reward for a company, but also satisfy budget constraints. We first approach the simpler online problem in which the customers arrive as a stationary Poisson process, and present an integrated algorithm that performs online optimization and online learning together. We then make the model more complicated but more realistic, treating the arrival processes as non-stationary Poisson processes. To deal with heterogeneous customer arrivals, we propose a time segmentation algorithm that converts a non-stationary problem into a series of stationary problems. Experiments conducted on large-scale synthetic data demonstrate the effectiveness and efficiency of our proposed approaches on solving constrained resource allocation problems.

Almost 37% of organizations have invested $5 million or more in cognitive technologies, states a survey by Deloitte. Inside and under every app we use every day there lies the revolution of technology. A revolution that started decades ago is now empowering organizations to deliver better and smarter services. The demand for artificial intelligence professionals has rapidly increased. But since AI adoption is still in its infancy there is a dearth for talent.