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.
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.
Choong-am Dojang is far from a typical Korean school. Its best pupils will never study history or math, nor will they receive traditional high-school diplomas. The academy, which operates above a bowling alley on a narrow street in northwestern Seoul, teaches only one subject: the game of Go, known in Korean as baduk and in Chinese as wei qi. Each day, Choong-am's students arrive at nine in the morning, find places at desks in a fluorescent-lit room, and play, study, memorize, and review games--with breaks for cafeteria meals or an occasional soccer match--until nine at night. Choong-am, which is the product of a merger between four top Go academies, is currently the biggest of a handful of dojangs in South Korea.
In 2012, IBM Watson went to medical school. So said The New York Times, announcing that the tech giant's artificially intelligent question-and-answer machine had begun a "stint as a medical student" at the Cleveland Clinic Lerner College of Medicine. This was just a metaphor. Clinicians were helping IBM train Watson for use in medical research. But as metaphors go, it wasn't a very good one.
In this course, you will learn what hyperparameters are, what Genetic Algorithm is, and what hyperparameter optimization is. In this course, you will apply Genetic Algorithm to optimize the performance of Support Vector Machines and Multilayer Perceptron Neural Networks. Hyperparameter optimization will be done on two datasets, a regression dataset for the prediction of cooling and heating loads of buildings, and a classification dataset regarding the classification of emails into spam and non-spam. The SVM and MLP will be applied on the datasets without optimization and compare their results to after their optimization. By the end of this course, you will have learnt how to code Genetic Algorithm in Python and how to optimize your Machine Learning algorithms for maximal performance.