Many data mining tasks cannot be completely addressed by automated processes, such as sentiment analysis and image classification. Crowdsourcing is an effective way to harness the human cognitive ability to process these machine-hard tasks. Thanks to public crowdsourcing platforms, e.g., Amazon Mechanical Turk and CrowdFlower, we can easily involve hundreds of thousands of ordinary workers (i.e., the crowd) to address these machine-hard tasks. In this tutorial, we will survey and synthesize a wide spectrum of existing studies on crowd-powered data mining. We first give an overview of crowdsourcing, and then summarize the fundamental techniques, including quality control, cost control, and latency control, which must be considered in crowdsourced data mining. Next we review crowd-powered data mining operations, including classification, clustering, pattern mining, outlier detection, knowledge base construction and enrichment. Finally, we provide the emerging challenges in crowdsourced data mining.

In addition to awarding Bachelors, Masters and Ph.D. degrees, the Allen School recognized two 2018 Alumni Impact Award recipients, Yaw Anokwa and Eileen Bjorkman.

Reliance Industries Limited today said it has completed an acquisition of close to 73 percent stake in artificial intelligence-based education technology provider Embibe. "RIL, Embibe and the other stakeholders have completed all the closing conditions and have successfully completed the sale and purchase of the shareholding of the existing investors in Embibe to RIL. With this transaction, RIL will hold 72.69 percent (on fully diluted basis) in Embibe," RIL said in a BSE filing. RIL in April had announced that it has entered into agreements to acquire close to 73 percent stake in education technology provider Individual Learning Private Ltd (Embibe) and plans to invest USD 180 million into the company over the next three years. Embibe will use the capital over the next three years towards deepening its R&D on AI in education, as well as business growth and geographic expansion, catering to students across K-12, higher education, professional skilling, vernacular languages and all curriculum categories across India and internationally.

Bill Gates, captured on April 19, 2018 in Berlin, Germany. Tech titans have a long history of making book recommendations. In 2015, Mark Zuckerberg launched a book club via Facebook, noting a must-read every two weeks. The year opened with Moisés Naím's The End of Power: From Boardrooms to Battlefields and Churches to States, Why Being In Charge Isn't What It Used to Be and closed out with David Deutsch's The Beginning of Infinity: Explanations that Transform the World; in between were titles on racism, genetics, religion, medicine, and sociology, and a smattering of science fiction novels. Jeff Bezos, owner of "bookstore killer" Amazon, had a book list on the platform in 2013 that included a variety of entrepreneur-focused books, like the iconic The Innovators Dilemma by Clayton Christensen and Built to Last: Successful Habits of Visionary Companies by Jim Collins.

Get your team access to Udemy's top 2,500 courses anytime, anywhere. This course is a workshop on logistic regression using R.

According to the "Unlocking the Economic Impact of Digital Transformation in Asia Pacific" conducted by Microsoft and IDC, it was found that 79% of jobs in Hong Kong will be transformed in the next three years, 60% of which will be redeployed to higher value roles, or reskilled to meet the need of the digital age. With the growing importance of Data Science, Big Data and AI in different industries, we launched the new Microsoft AI Certification Programme in March with different tracks covering AI, Big Data, and Data Science to enable IT and business professionals to get equipped with future-ready skill sets for career development in this competitive job market. Moreover, we are extending our support to University Students by organizing free AI workshops to understand the concept and impact of AI in real-life applications. The first workshop of our Microsoft AI Workshop Series was held at The Hong Kong Polytechnic University (PolyU), with overwhelming attendance of more than 160 students participating with engaged conversations and follow-up questions on the AI Certification Programme. Following the success of our first Microsoft AI Workshop at PolyU, we have continued the series at Hong Kong University of Science and Technology (HKUST) and The Chinese University of Hong Kong (CUHK), which attracted a record-high of 200 and 230 attendees for each session. Students were interested to learn more about AI use-cases and our different tracks in our AI Certification Programme.

Google has released its AI policy for the first time since the company started working on this technology. The new policy will govern applications and other services under Google's domain that use Artificial Intelligence to get work done. The company's chief Sundar Pichai, released a detailed document about the objective of the applications that will use AI as a tool. At the same time, the document also mentions the objectives that AI applications won't pursue to restrict the use of the technology. However, Google went on to confirm that they will continue to work with government bodies and military.

You say you're a parent or teacher investigating robot kits for children? And you don't want a simple solution with a single purpose: you want the child to experience science, technology, engineering, and math? You want a kit that teaches all four categories, from piecing together the foundation to wiring the appendages to programming the "brain" using software. That's where our list of robot kits for kids comes in. Most of the robot kits listed below are tied to terms such as STEM, Arduino, and Blockly.

Time series classification is an increasing research topic due to the vast amount of time series data that are being created over a wide variety of fields. The particularity of the data makes it a challenging task and different approaches have been taken, including the distance based approach. 1-NN has been a widely used method within distance based time series classification due to it simplicity but still good performance. However, its supremacy may be attributed to being able to use specific distances for time series within the classification process and not to the classifier itself. With the aim of exploiting these distances within more complex classifiers, new approaches have arisen in the past few years that are competitive or which outperform the 1-NN based approaches. In some cases, these new methods use the distance measure to transform the series into feature vectors, bridging the gap between time series and traditional classifiers. In other cases, the distances are employed to obtain a time series kernel and enable the use of kernel methods for time series classification. One of the main challenges is that a kernel function must be positive semi-definite, a matter that is also addressed within this review. The presented review includes a taxonomy of all those methods that aim to classify time series using a distance based approach, as well as a discussion of the strengths and weaknesses of each method.

Sample efficiency is critical in solving real-world reinforcement learning problems, where agent-environment interactions can be costly. Imitation learning from expert advice has proved to be an effective strategy for reducing the number of interactions required to train a policy. Online imitation learning, a specific type of imitation learning that interleaves policy evaluation and policy optimization, is a particularly effective framework for training policies with provable performance guarantees. In this work, we seek to further accelerate the convergence rate of online imitation learning, making it more sample efficient. We propose two model-based algorithms inspired by Follow-the-Leader (FTL) with prediction: MoBIL-VI based on solving variational inequalities and MoBIL-Prox based on stochastic first-order updates. When a dynamics model is learned online, these algorithms can provably accelerate the best known convergence rate up to an order. Our algorithms can be viewed as a generalization of stochastic Mirror-Prox by Juditsky et al. (2011), and admit a simple constructive FTL-style analysis of performance. The algorithms are also empirically validated in simulation.