Companies usually give college students, teachers, faculty and staff some solid discounts on both hardware and software. Today, Apple is doing just that with a new audio and video education bundle. The software pack offers Final Cut Pro X, Logic Pro X, Motion 5, Compressor 4 and MainStage 3 for $200. For reference, that's the price you would typically pay for Logic Pro X and Final Cut Pro X will set you back $300. The bundle is available for both K-12 and higher education institutions so computer labs and classrooms can use pro-level software as learning tools.

In this article, we revisit the most fundamental statistics theorem, talking in layman terms. We investigate a special but interesting and useful case, which is not discussed in textbooks, data camps, or data science classes. This article is part of a series about off-the-beaten-path data science and mathematics, offering a fresh, original and simple perspective on a number of topics. Previous articles in this series can be found here and also here. The theorem discussed here is the central limit theorem.

Title: What are the phenomena to be explained?

The development of artificial intelligence (AI) has had a huge influence on today's society, as ongoing discussions evaluate the impacts of creating machines and computer systems that can react and perform like humans. These systems can process information in a more cognitive way, making them capable of more human-like functions like learning, decision-making, and visual perception. Hollywood portrayals of hyper-intelligent robots taking over the planet might make artificial intelligence seem intimidating, but there is a lot that can be gained by through these advanced computer systems. Without the element of human error, intelligent machines are capable of unmatched precision and accuracy, and since they don't require fundamental human needs like oxygen or food, they can perform tasks with far fewer limitations. In fact, AI is already popping up everywhere in our daily lives – through social media recommendations, virtual assistants on our smartphones, and even self-driving cars.

Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes.

Nothing reveals as much about a society, and its future, as its high schools. Yet amid accelerating change -- widening inequality, unprecedented globalization and technological advances -- they've woefully lagged behind. There are, of course, exceptions. Follow OZY's special series High School, Disrupted to find out about the global leaders, cutting-edge trends and big ideas reimagining secondary education -- for the better. From Siri handling our schedules to smart cars driving themselves, artificial intelligence (AI) has turned our world upside down -- except in education.

Machine learning is a growing field, used in everything from the basics of anti-spam functions to the complexities of self-driving cars. As this is a constantly adapting technology, companies seeking to take advantage of the system for functions like analytics may have trouble finding the best place to begin. So what is the first step for a tech department that wants to start using machine learning to improve its data analytics? Even if you rely on outside expertise, it is important to understand what machine learning can and can't do with data. Stanford, Caltech and others offer online classes on Coursera that are very good.

Automated machine learning has become a topic of considerable interest over the past several months. A recent KDnuggets blog competition focused on this topic, and generated a handful of interesting ideas and projects. Of note, our readers were introduced to Auto-sklearn, an automated machine learning pipeline generator, via the competition, and learned more about the project in a follow-up interview with its developers. Prior to that competition, however, KDnuggets readers were introduced to TPOT, "your data science assistant," an open source Python tool that intelligently automates the entire machine learning process. For scikit-learn-compatible datasets, TPOT can automatically optimize a series of feature preprocessors and machine learning models that maximize the dataset's cross-validation accuracy, and outputs the optimal model as Python code leveraging scikit-learn.

One month into the New Year, now is a good time for every chief human resource officer and his/her team to take stock of the big bets they should be making in 2017. After a tumultuous 2016 on this front, we're seeing stronger interest from CEOs and CHROs in clearly expressing their company's brand purpose and values, especially to establish a stronger connection with talent and customers. When evaluating a potential employer, job candidates cite "culture and values" as their most important consideration, according to the latest LinkedIn Global Recruiting Trends Report. "Mission and vision" is the third most important consideration. Given the CHRO's role as the top HR leader, he or she plays a crucial part in articulating what it means to work for your organization, and why talented people should commit to it over other organizations.

The Leverhulme Centre for the Future of Intelligence (CFI) invites applications for a postdoctoral Research Associate for the project'Policy, Responsible Innovation and the Future of AI'. The appointment will be for 3 years, and is based in Cambridge. CFI is an exciting new interdisciplinary research centre addressing the challenges and opportunities posed by artificial intelligence (AI). Funded by the Leverhulme Trust, CFI is based at the University of Cambridge, with partners in the University of Oxford, Imperial College, and UC Berkeley, and close links with industry partners and policymakers. This project examines the prospects for a robust safety and benefits culture within the AI industry, in anticipation of the development of increasingly powerful AI systems that will present ever-greater real-world opportunities and challenges.