Currently, college-going students are taking longer to graduate than their parental generations. Further, in the United States, the six-year graduation rate has been 59% for decades. Improving the educational quality by training better-prepared students who can successfully graduate in a timely manner is critical. Accurately predicting students' grades in future courses has attracted much attention as it can help identify at-risk students early so that personalized feedback can be provided to them on time by advisors. Prior research on students' grade prediction include shallow linear models; however, students' learning is a highly complex process that involves the accumulation of knowledge across a sequence of courses that can not be sufficiently modeled by these linear models. In addition to that, prior approaches focus on prediction accuracy without considering prediction uncertainty, which is essential for advising and decision making. In this work, we present two types of Bayesian deep learning models for grade prediction. The MLP ignores the temporal dynamics of students' knowledge evolution. Hence, we propose RNN for students' performance prediction. To evaluate the performance of the proposed models, we performed extensive experiments on data collected from a large public university. The experimental results show that the proposed models achieve better performance than prior state-of-the-art approaches. Besides more accurate results, Bayesian deep learning models estimate uncertainty associated with the predictions. We explore how uncertainty estimation can be applied towards developing a reliable educational early warning system. In addition to uncertainty, we also develop an approach to explain the prediction results, which is useful for advisors to provide personalized feedback to students.

Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka "deep learning") approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision.

In addition, the money will be used for "cutting-edge" AI and robotics that can "operate in extreme and hazardous environments," including nuclear energy, space and deep mining environments. Today's Budget also unpacked the government's plans -- again, hinted at in last year's Autumn Statement -- to support fibre broadband and 5G connectivity in the UK. Starting this year, the treasury has promised to spend £200 million on a suite of local projects that will "test ways to accelerate market delivery of new full-fibre broadband networks." These include connection vouchers for businesses, new connections for schools, hospitals and other public sector buildings, and a push to bundle local public sector users "to create enough broadband demand to reduce the financial risk of building new full-fibre networks," Google Fiber style. We heard a lot about 5G at Mobile World Congress, however the super-fast network technology is still a way off.

This is programming assignment 3 from the course T81-855: Applications of Deep Learning at Washington University in St. Louis. All students must create a Kaggle account and submit a solution. Once you have submitted your solution entry log into Blackboard (at WUSTL) and submit a single file telling me your Kaggle name on the leaderboard (you do not need to register to Kaggle with your real name). This competition will be visible to the public, so there may be non-student submissions as well as student. The data set for this competition consists of 7 input columns that should be used to predict an outcome.