Education
RIT faculty earns NSF CAREER award to study human behavior using machine learning
A Rochester Institute of Technology professor has earned a prestigious National Science Foundation award to use computers to better understand human behavior and social interaction. Ifeoma Nwogu, an assistant professor of computer science, received an NSF Faculty Early Career Development (CAREER) award and grant for her five-year project. She aims to study human behavior in a new way, by using machine learning techniques to analyze and find patterns in the many signals that individuals display during social interactions. Her work will specifically look at groups working in science, technology, engineering and math (STEM), with the aim of supporting underrepresented groups in STEM. "In a conversation, people are constantly displaying and processing different non-verbal signals, such as how fast someone is talking or the facial expressions they are making," said Nwogu.
Transforming Online Learning With Artificial Intelligence
As higher education costs continue to rise, students bear the ultimate burden of choosing the right school, major, and delivery format to maximize post-graduation success. Unlike previous generations, millennials and adult learners are searching for alternatives to full-time, on-campus programs, and universities are eager to offer non-traditional routes to a degree. Distance learning programs have existed since the 1980s, but technological innovation, content scalability, and widespread mobile adoption have enabled the online degree program to be a competitive option for aspiring students. Long gone are the days of aggressive marketing tactics and empty promises made by degree mills and unaccredited for-profit universities. Today, a learner can enroll in competitive bachelor's and master's programs at U Penn, Columbia, Johns Hopkins, NYU, and more.
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Analysis: Could artificial intelligence replace GCSEs?
Elite independent schools are currently in talks with a technology company exploring a new artificial intelligence programme that could replace GCSEs. Yet while a digital alternative to the GCSE that some schools view as "increasingly dry" might sound promising, how feasible would it really be? Mike Buchanan, executive director of the Headmasters' and Headmistresses' Conference, has spoken of how this could create a far more "personalised" style of learning, with computer programmes constructing a digitised profile of each pupil's skills. And arguably, the historic reason for GCSEs no longer exists, given that most students do not leave school at 16. So the idea of a bespoke digital learning platform is attractive, and according to one source in the private school sector, senior figures at Ofsted are interested. Rather than studying a raft of GCSEs, pupils would learn from an online programme alongside their usual class teaching.
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Learning Python: Powerful Object-Oriented Programming GC Coupons
Get a comprehensive, in-depth introduction to the core Python language with this hands-on book. Based on author Mark Lutz's popular training course, this updated fifth edition will help you quickly write efficient, high-quality code with Python. It's an ideal way to begin, whether you're new to programming or a professional developer versed in other languages. Complete with quizzes, exercises, and helpful illustrations, this easy-to-follow, self-paced tutorial gets you started with both Python 2.7 and 3.3-- the latest releases in the 3.X and 2.X lines--plus all other releases in common use today. You'll also learn some advanced language features that recently have become more common in Python code.
Hands-On Neural Networks with TensorFlow 2.0 GC Coupons
TensorFlow, the most popular and widely used machine learning framework, has made it possible for almost anyone to develop machine learning solutions with ease. With TensorFlow (TF) 2.0, you'll explore a revamped framework structure, offering a wide variety of new features aimed at improving productivity and ease of use for developers. This book covers machine learning with a focus on developing neural network-based solutions. You'll start by getting familiar with the concepts and techniques required to build solutions to deep learning problems. As you advance, you'll learn how to create classifiers, build object detection and semantic segmentation networks, train generative models, and speed up the development process using TF 2.0 tools such as TensorFlow Datasets and TensorFlow Hub.
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