Today's digital economy is blurring the boundaries between computer science and economics -- in Silicon Valley, on Wall Street, and increasingly on university campuses. Yale undergraduates interested in both fields can pursue the Computer Science and Economics (CSEC) interdepartmental degree program, which launched in fall 2019, with coursework covering topics such as machine learning and computational finance. Philipp Strack, CSEC's inaugural director of undergraduate studies, is comfortable straddling multiple disciplines. With an academic background in economics and mathematics, his research reflects this broad and interdisciplinary outlook -- ranging from behavioral economics and neuroscience to auction design, market design, optimization, and pure probability theory. Strack, an associate professor of economics in the Faculty of Arts and Sciences, recently spoke to YaleNews about the real-world implications of this work, what the CSEC program offers students, and how it bridges these critical fields.

The age of being able to speak to our computers just as we do with other humans is finally upon us but voice-activated assistants like Siri, Alexa, and Google Home haven't proven quite as revolutionary -- or inclusive -- as we'd hoped they'd be. While these systems make a commendable effort to accurately interpret commands regardless of whether you picked up your accent in Houston or Hamburg, for users with heavier or less common accents such as Caribbean or Cockney, requests to their digital assistants are roundly ignored. In her essay "Siri Disciplines" for Your Computer Is on Fire from the MIT Press, Towson University professor Dr. Halcyon M. Lawrence, examens some of the more glaring shortcomings of this nascent technology, how those preventable failures have effectively excluded a sizeable number of potential users and the western biases underpinning the issue. Used with permission of the publisher, MIT Press. Voice technologies are routinely described as revolutionary.

Human intelligence reflects our brain's ability to learn. Computer systems that act like humans use artificial intelligence. That means these systems are under the control of computer programs that can learn. Just as people do, computers can learn to use data and then make decisions or assessments from what they've learned. Called machine learning, it's part of the larger field of artificial intelligence.

This is an intermediate-level free artificial intelligence course. This course will teach the basics of modern AI as well as some of the representative applications of AI including machine learning, probabilistic reasoning, robotics, computer vision, and natural language processing. To understand this course, you should have some previous understanding of probability theory and linear algebra.

"Mitchell knows what she's talking about. Artificial Intelligence has significantly improved my knowledge when it comes to automation technology, [but] the greater benefit is that it has also enhanced my appreciation for the complexity and ineffability of human cognition."―John Warner, Chicago Tribune "Without shying away from technical details, this survey provides an accessible course in neural networks, computer vision, and natural-language processing, and asks whether the quest to produce an abstracted, general intelligence is worrisome . . . Mitchell's view is a reassuring one." AI isn't for the faint of heart, and neither is this book for nonscientists . . .

I started learning about neural networks a few months ago and I quickly realized that there were certain mathematical topics that were essential to understand in order to understand neural networks. Calculus and linear algebra are really important topics to be familiar with and it had been years since I had taken those courses in university. I thought it would be useful to consolidate some learning resources in one place that would help a beginner get started. There are tons of libraries out there that implement the complex mathematics for us and you definitely don't need a math degree to build neural networks. However, having a basic understanding of the math behind neural networks is really important to understanding how they work in case you need to debug or optimize your algorithm.

And after submitting the form, they analyzed my application, and based on the details I provided, they offered me a 75% discount. And I also received 100$ worth of AWS credits, which was amazing. You can also check for Udacity Financial Support option here. So this was my story how I had chosen this Udacity Deep Learning Nanodegree program.

The OSSU curriculum is a complete education in computer science using online materials. It's for those who want a proper, well-rounded grounding in concepts fundamental to all computing disciplines, and for those who have the discipline, will, and (most importantly!) good habits to obtain this education largely on their own, but with support from a worldwide community of fellow learners. It is designed according to the degree requirements of undergraduate computer science majors, minus general education (non-CS) requirements, as it is assumed most of the people following this curriculum are already educated outside the field of CS. The courses themselves are among the very best in the world, often coming from Harvard, Princeton, MIT, etc., but specifically chosen to meet the following criteria. When no course meets the above criteria, the coursework is supplemented with a book.

In this paper, we propose a framework for predicting frame errors in the collaborative spectrally congested wireless environments of the DARPA Spectrum Collaboration Challenge (SC2) via a recently collected dataset. We employ distributed deep edge learning that is shared among edge nodes and a central cloud. Using this close-to-practice dataset, we find that widely used federated learning approaches, specially those that are privacy preserving, are worse than local training for a wide range of settings. We hence utilize the synthetic minority oversampling technique to maintain privacy via avoiding the transfer of local data to the cloud, and utilize knowledge distillation with an aim to benefit from high cloud computing and storage capabilities. The proposed framework achieves overall better performance than both local and federated training approaches, while being robust against catastrophic failures as well as challenging channel conditions that result in high frame error rates.

Locally Linear Embedding (LLE) is a nonlinear spectral dimensionality reduction and manifold learning method. It has two main steps which are linear reconstruction and linear embedding of points in the input space and embedding space, respectively. In this work, we propose two novel generative versions of LLE, named Generative LLE (GLLE), whose linear reconstruction steps are stochastic rather than deterministic. GLLE assumes that every data point is caused by its linear reconstruction weights as latent factors. The proposed GLLE algorithms can generate various LLE embeddings stochastically while all the generated embeddings relate to the original LLE embedding. We propose two versions for stochastic linear reconstruction, one using expectation maximization and another with direct sampling from a derived distribution by optimization. The proposed GLLE methods are closely related to and inspired by variational inference, factor analysis, and probabilistic principal component analysis. Our simulations show that the proposed GLLE methods work effectively in unfolding and generating submanifolds of data.