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

#artificialintelligence

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

#artificialintelligence

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.


Vol 13, No 10 (2018) International Journal of Emerging Technologies in Learning (iJET)

#artificialintelligence

HOy traemos a este espacio el último número el Vol 13, No 10 (2018) del International Journal of Emerging Technologies in Learning (iJET) This interdisciplinary journal aims to focus on the exchange of relevant trends and research results as well as the presentation of practical experiences gained while developing and testing elements of technology enhanced learning. So it aims to bridge the gap between pure academic research journals and more practical publications. So it covers the full range from research, application development to experience reports and product descriptions. Readers don't have to pay any fee. Vol 13, No 10 (2018) Table of Contents Papers Innovative English Classroom Teaching Based on Online Computer Technology in Rural Middle and Primary Schools Application of Brain Neural Network in Personalized English Education System Songlin Yang, Min Zhang A Generic Tool for Generating and Assessing Problems Automatically using Spreadsheets Maria Assumpció Rafart Serra, Andrea Bikfalvi, Josep Soler Masó, Jordi Poch Garcia A High Security Distance Education Platform Infrastructure Based on Private Cloud Jingtai Ran, Kepeng Hou, Kegang Li, Niya Dai Student Performance Prediction Model Based on Discriminative Feature Selection Haixia Lu, Jinsong Yuan An Eight-Layer Model for Mathematical Cognition Marios A. Pappas, Athanasios S. Drigas, Fotini Polychroni Design and Implementation of University Art Education Management System Based on JAVA Technology The Design and Application of Flip Classroom Teaching Based on Computer Technology Jia Li, Xiaoxia Zhang, Zijun Hu Feature Extraction and Learning Effect Analysis for MOOCs Users Based on Data Mining Intelligent System for College English Listening and Writing Training Development of an Accounting Skills Simulation Practice System Based on the B/S Architecture Jianmei Liu, Rong Fu Teaching Quality Evaluation and Scheme Prediction Model Based on Improved Decision Tree Algorithm Sujuan Jia, Yajing Pang Blended Learning Innovation Model among College Students Based on Internet Score Prediction Model of MOOCs Learners Based on Neural Network Yuan Zhang, Wenbo Jiang Design and Implementation of the Online Computer-Assisted Instruction System Based on Object-Oriented Analysis Technology Wenbo Zhou, Lei Shi, Jian Chen Matlab-Realized Visual A* Path Planning Teaching Platform Communication Jigsaw: A Teaching Method that Promotes Scholarly Communication A Tablet-Computer-Based Tool to Facilitate Accurate Self-Assessments in Third- and Fourth-Graders Denise Villanyi, Romain Martin, Philipp Sonnleitner, Christina Siry, Antoine Fischbach Short Papers Application of Blockchain Technology in Online Education Han Sun, Xiaoyue Wang, Xinge Wang The Grading Multiple Choice Tests System via Mobile Phone using Image Processing Technique Worawut Yimyam, Mahasak Ketcham Offline Support Model for Low Bandwidth Users to Survive in MOOCs International Journal of Emerging Technologies in Learning.


Data Mining with Rattle Udemy

@machinelearnbot

Data Mining with Rattle is a unique course that instructs with respect to both the concepts of data mining, as well as to the "hands-on" use of a popular, contemporary data mining software tool, "Data Miner," also known as the'Rattle' package in R software. Rattle is a popular GUI-based software tool which'fits on top of' R software. The course focuses on life-cycle issues, processes, and tasks related to supporting a'cradle-to-grave' data mining project. These include: data exploration and visualization; testing data for random variable family characteristics and distributional assumptions; transforming data by scale or by data type; performing cluster analyses; creating, analyzing and interpreting association rules; and creating and evaluating predictive models that may utilize: regression; generalized linear modeling (GLMs); decision trees; recursive partitioning; random forests; boosting; and/or support vector machine (SVM) paradigms. It is both a conceptual and a practical course as it teaches and instructs about data mining, and provides ample demonstrations of conducting data mining tasks using the Rattle R package.