When the Montour School District launched America's first Artificial Intelligence Middle School program in the fall of 2018, many questions arose. How? (Just to name a few). But, as a student-centered and future-focused district, the thought process was not if we should teach AI, but what if we don't teach AI? Also, why isn't everyone teaching AI? Through a series of courses developed and implemented by Montour team members and partners, the AI program officially launched in October 2018. To date, hundreds of classes have already been taught to students in areas of AI Ethics, AI Autonomous Robotics, AI Computer Science, and AI Music. The goal for the program is to make an all-inclusive AI program for all middle school students that is relevant and meaningful in a world where children live and prepare them for a future where they will thrive.

Given the widespread popularity of spectral clustering (SC) for partitioning graph data, we study a version of constrained SC in which we try to incorporate the fairness notion proposed by Chierichetti et al. (2017). According to this notion, a clustering is fair if every demographic group is approximately proportionally represented in each cluster. To this end, we develop variants of both normalized and unnormalized constrained SC and show that they help find fairer clusterings on both synthetic and real data. We also provide a rigorous theoretical analysis of our algorithms. While there have been efforts to incorporate various constraints into the SC framework, theoretically analyzing them is a challenging problem. We overcome this by proposing a natural variant of the stochastic block model where h groups have strong inter-group connectivity, but also exhibit a "natural" clustering structure which is fair. We prove that our algorithms can recover this fair clustering with high probability.

Artificial intelligence may still be an emerging technology, but chances are you're already using it in your everyday life. AI is what is powers iPhone's Siri and Google Assistant. Gmail's smart replies, online product suggestions, and directions for the fastest route -- with traffic included -- from one place to another are all examples of AI coming into play. AI, which allows computers and other machinery to learn and adapt to its surroundings, is also active in schools and in classrooms. It runs in many educational and tutoring apps, and digital curriculum tools use this technology to assess a student's performance and suggest an individualized learning plan to help them improve their understanding of a subject.

This bundle includes 8 courses that will immerse you in the fields of Machine Learning & Analytics by teaching you the skills used to master both theory & practice. Learn how to install Python, and then use it to perform sentiment analysis, build a recommendation system, and so much more. With over 40 hours of expert instruction, by the time you've completed this bundle of courses, you'll have a firm grasp of core machine learning concepts and be on your way to applying this essential technology in your career.

Does your organization invest a huge amount of time, energy and money in training employees? If yes, there is now a better way to manage such training. Smart computer programs can take all the loads off your shoulders. Chatbots are one such artificial intelligence that can minimize your business efforts and help you graduate to better customer engagement, more effective employee training, greater productivity, and increased bottom line. This demonstrates that AI-powered programs such as chatbots are gradually transforming the business landscape everywhere by simulating human beings.

In this tutorial, you will learn how to perform regression using Keras and Deep Learning. You will learn how to train a Keras neural network for regression and continuous value prediction, specifically in the context of house price prediction. Today's post kicks off a 3-part series on deep learning, regression, and continuous value prediction. We'll be studying Keras regression prediction in the context of house price prediction: Unlike classification (which predicts labels), regression enables us to predict continuous values. For example, classification may be able to predict one of the following values: {cheap, affordable, expensive}.

After working as Electronic Engineer, I decided to change my career path to Data Scientist . To reach my Data Science career goal I have started to review Moocs about this field. All these courses are explain core machine learning algorithms. Also, in Coursera's Machine Learning course Andrew NG explained the mathematical background of these algorithms. If you want to learn what Machine Learning is and the way that you can use it, i strongly suggest you to take these entire three courses.

Bayesian online changepoint detection (BOCPD) (Adams & MacKay, 2007) offers a rigorous and viable way to identity changepoints in complex systems. In this work, we introduce a Stein variational online changepoint detection (SVOCD) method to provide a computationally tractable generalization of BOCPD beyond the exponential family of probability distributions. We integrate the recently developed Stein variational Newton (SVN) method (Detommaso et al., 2018) and BOCPD to offer a full online Bayesian treatment for a large number of situations with significant importance in practice. We apply the resulting method to two challenging and novel applications: Hawkes processes and long short-term memory (LSTM) neural networks. In both cases, we successfully demonstrate the efficacy of our method on real data.

Python is one of the most sought after programming language. This course will teach you Object Oriented Programming, using Python as the programming language. By learning OOP using Python, you are taking your Python skills to the intermediate level from where you can pursue other advanced Python modules.

These transformations will open up new opportunities for individuals, the economy, and society, but they have the potential to disrupt the current livelihoods of millions of Americans. Whether AI leads to unemployment and increases in inequality over the long-run depends not only on the technology itself but also on the institutions and policies that are in place. This report examines the expected impact of AI-driven automation on the economy, and describes broad strategies that could increase the benefits of AI and mitigate its costs. Economics of AI-Driven Automation Technological progress is the main driver of growth of GDP per capita, allowing output to increase faster than labor and capital. One of the main ways that technology increases productivity is by decreasing the number of labor hours needed to create a unit of output.