machine learning


Optimization for large scale machine learning at University of Bath on FindAPhD.com

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ART-AI CDT studentships are available on a competition basis for UK and EU students for up to 4 years. Funding will cover UK/EU tuition fees as well as providing maintenance at the UKRI doctoral stipend rate (£15,009 per annum in 2019/20, increased annually in line with the GDP deflator) and a training support fee of £1,000 per annum. FTE Category A staff submitted: 24.00


Hands-on with Feature Selection Techniques: Embedded Methods

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Embedded methods complete the feature selection process within the construction of the machine learning algorithm itself. In other words, they perform feature selection during the model training, which is why we call them embedded methods. A learning algorithm takes advantage of its own variable selection process and performs feature selection and classification/regression at the same time. The embedded method solves both issues we encountered with the filter and wrapper methods by combining their advantages. In this article, we'll explore a few specific methods that use embedded feature selection: regularization and tree-based methods.


Why Unsupervised Machine Learning is the Future of Cybersecurity

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As we move towards a future where we lean on cybersecurity much more in our daily lives, it's important to be aware of the differences in the types of AI being used for network security. Over the last decade, Machine Learning has made huge progress in technology with Supervised and Reinforcement learning, in everything from photo recognition to self-driving cars. However, Supervised Learning is limited in its network security abilities like finding threats because it only looks for specifics that it has seen or labeled before, whereas Unsupervised Learning is constantly searching the network to find anomalies. Machine Learning comes in a few forms: Supervised, Reinforcement, Unsupervised and Semi-Supervised (also known as Active Learning). Supervised Learning relies on a process of labeling in order to "understand" information.


Apple's Acquisition of Xnor.ai Aims to Deliver TinyML to Edge Devices

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The News: Last week, Apple's acquisition of Xnor.ai was reported, no doubt aiming to deliver TinyML to edge devices. Xnor.ai, a Seattle startup specializing in low-power, edge-based artificial intelligence (AI) tools. Spun off from the Allen Institute for Artificial Intelligence, the three-year-old startup's technology embeds AI on the edge, enabling facial recognition, natural language processing, augmented reality, and other ML-driven capabilities to be executed on low-power devices rather than relying on the cloud. Analyst Take: Developers of AI applications for edge deployment are doing their work in a growing range of frameworks and deploying their models to myriad hardware, software, and cloud environments. This complicates the task of making sure that each new AI model is optimized for fast inferencing on its target platform, a burden that has traditionally required manual tuning.


AI and the Auteur: Implications of Using Artificial Intelligence in Film Studio Decision-Making

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The global movie industry generated over $43 billion in revenue in 2018, of which the United States' contribution alone topped more than $11 billion. Yet, these seemingly impressive headline figures can obscure the fact that year-on-year growth has been a sluggish 2 per cent over the last several years, with market researchers forecasting further stagnation. Given the inherent financial risk involved in film making, some now believe artificial intelligence, rather than human expertise, is best placed to select which films are most likely to provide suitable returns on investment. In early January 2020, Warner Bros signed a deal with Cinelytic, a Los Angeles-based artificial intelligence company which, according to the press release, aims to help content creators make faster, better-informed decisions through predictive analytics. Belgium's ScriptBook provides a similar service, touted as "artificially intelligent script analysis and box office forecasting".


Duke University Health System Joins LeanTaaS to Deliver Keynote

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Improving operating room capacity management through data analytics and machine learning will be the breakfast keynote topic of discussion at the upcoming 2020 OR Business Management Conference. Ashley Walsh, senior director of client services at LeanTaaS, Inc., a Silicon Valley software innovator that increases patient access and transforms operational performance for healthcare providers, and Melissa Pressley, management engineer at Duke University Health System (DUHS), will address the audience on Thursday, Jan. 30, at 7:30 a.m. in the Global Ballroom of the Bonaventure Resort & Spa in Weston, Florida. "Improving OR utilization and improving surgeon access to OR time significantly enhances the financial results for hospitals and health systems, increases patient access, and facilitates surgeon recruitment and retention" "DUHS has leveraged EHR data to improve OR access with mobile and web technologies and increase accountability with surgeon-centric metrics and reporting to help our surgeons better understand the "why" behind OR metrics," said Pressley. "I'm looking forward to sharing how DUHS and LeanTaaS have enhanced the patient experience while balancing surgeon needs, among other improvements." DUHS is among several leading health systems in the U.S. that have deployed the LeanTaaS iQueue for Operating Rooms solution to effect data-driven changes to their approach to capacity management.


New artificial intelligence inspired by the functioning of the human brain

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Artificial Intelligence (AI) has enabled the development of high-performance automatic learning techniques in recent years. However, these techniques are often applied task by task, which implies that an intelligent agent trained for one task will perform poorly on other tasks, even very similar ones. To overcome this problem, researchers at the University of Liège (ULiège) have developed a new algorithm based on a biological mechanism called neuromodulation. This algorithm makes it possible to create intelligent agents capable of performing tasks not encountered during training. This novel and exceptional result is presented this week in the magazine PLOS ONE.



Python: Implementing a k-means algorithm with sklearn

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Originally posted by Michael Grogan. The below is an example of how sklearn in Python can be used to develop a k-means clustering algorithm. The purpose of k-means clustering is to be able to partition observations in a dataset into a specific number of clusters in order to aid in analysis of the data. From this perspective, it has particular value from a data visualisation perspective. The particular example used here is that of stock returns.


Small and Medium Enterprises (SMEs) rush for Machine Learning - BizAcuity Solutions Pvt. Ltd.

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Back in in 1959, Arthur Samuel coined the term Machine Learning with a purpose. He wanted the computer systems to learn from data without being programmed. This latest approach not only helps the world perform computing processes in an efficient and cost-effective manner but also helps manage the gamut of data-driven affairs. Machine learning starts and sparks with the generic algorithms. It does mining, compiling, analyzing massive data and way beyond.