AI Nanodegree Program Syllabus: Term 2 (Deep Learning), In Depth

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Here at Udacity, we are tremendously excited to announce the kick-off of the second term of our Artificial Intelligence Nanodegree program. Because we are able to provide a depth of education that is commensurate with university education; because we are bridging the gap between universities and industry by providing you with hands-on projects and partnering with the top industries in the field; and last but certainly not least, because we are able to bring this education to many more people across the globe, at a cost that makes a top-notch AI education realistic for all aspiring learners. During the first term, you've enjoyed learning about Game Playing Agents, Simulated Annealing, Constraint Satisfaction, Logic and Planning, and Probabilistic AI from some of the biggest names in the field: Sebastian Thrun, Peter Norvig, and Thad Starner. Term 2 will be focused on one of the cutting-edge advancements of AI -- Deep Learning. In this Term, you will learn about the foundations of neural networks, understand how to train these neural networks with techniques such as gradient descent and backpropagation, and learn different types of architectures that make neural networks work for a variety of different applications.


Neural Educational Recommendation Engine (NERE)

arXiv.org Machine Learning

Quizlet is the most popular online learning tool in the United States, and is used by over 2/3 of high school students, and 1/2 of college students. With more than 95% of Quizlet users reporting improved grades as a result, the platform has become the de-facto tool used in millions of classrooms. In this paper, we explore the task of recommending suitable content for a student to study, given their prior interests, as well as what their peers are studying. We propose a novel approach, i.e. Neural Educational Recommendation Engine (NERE), to recommend educational content by leveraging student behaviors rather than ratings. We have found that this approach better captures social factors that are more aligned with learning. NERE is based on a recurrent neural network that includes collaborative and content-based approaches for recommendation, and takes into account any particular student's speed, mastery, and experience to recommend the appropriate task. We train NERE by jointly learning the user embeddings and content embeddings, and attempt to predict the content embedding for the final timestamp. We also develop a confidence estimator for our neural network, which is a crucial requirement for productionizing this model. We apply NERE to Quizlet's proprietary dataset, and present our results. We achieved an R^2 score of 0.81 in the content embedding space, and a recall score of 54% on our 100 nearest neighbors. This vastly exceeds the recall@100 score of 12% that a standard matrix-factorization approach provides. We conclude with a discussion on how NERE will be deployed, and position our work as one of the first educational recommender systems for the K-12 space.


Coursera Coupons Min 10% off 100% Free Courses Student Offer

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Learn Machine Learning Stanford University Professor and earn certification to full proof your career. Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI.


An AI algorithm passed a science test. Here's what you should know.

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This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. Last week, the Allen Institute for Artificial Intelligence (AI2) introduced Aristo, an artificial intelligence model that scored above 90 percent on an 8th grade science test and 80 percent on a 12th-grade exam. Passing a science test might sound mundane, if you're not familiar with how deep learning algorithms, the current bleeding edge of AI, work. After all, AI is already performing tasks such as diagnosing cancer, detecting fraud and playing complicated games, which are much more complicated than answering simple science questions about the moon and squirrel populations. But despite its fascinating achievements, deep learning struggles when it comes to tackling problems that require reasoning and commonsense.


Next Target for IBM's Watson? Third-Grade Math

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It knew enough about medical diagnoses and literature to beat "Jeopardy!" Now, an IBMcomputer platform called Watson is taking on something really tough: teaching third-grade math. For the past two years, the IBM Foundation has worked with teachers and their union, the American Federation of Teachers, to build Teacher Advisor, a program that uses artificial-intelligence technology to answer questions from educators and help them build personalized lesson plans. By the end of the year, it will be available free to third-grade math teachers across the country and will add subject areas and grade levels over time. "The idea was to build a personal adviser, so a teacher would be able to find the best lesson and then customize the lesson based upon their classroom needs," said Stanley S. Litow, president of the IBM Foundation.