Accurately predicting students' future performance based on their tracked academic records in college programs is crucial for effectively carrying out necessary pedagogical interventions to ensure students' on-time graduation. Although there is a rich literature on predicting student performance in solving problems and studying courses using data-driven approaches, predicting student performance in completing college programs is much less studied and faces new challenges, mainly due to the diversity of courses selected by students and the requirement of continuous tracking and incorporation of students' evolving progresses. In this paper, we develop a novel algorithm that enables progressive prediction of students' performance by adapting ensemble learning techniques and utilizing education-specific domain knowledge. We prove its prediction performance guarantee and show its performance improvement against benchmark algorithms on a real-world student dataset from UCLA.

We propose a lifelong learning system that has the ability to reuse and transfer knowledge from one task to another while efficiently retaining the previously learned knowledge-base. Knowledge is transferred by learning reusable skills to solve tasks in Minecraft, a popular video game which is an unsolved and high-dimensional lifelong learning problem. These reusable skills, which we refer to as Deep Skill Networks, are then incorporated into our novel Hierarchical Deep Reinforcement Learning Network (H-DRLN) architecture using two techniques: (1) a deep skill array and (2) skill distillation, our novel variation of policy distillation (Rusu et. al. 2015) for learning skills. Skill distillation enables the H-DRLN to efficiently retain knowledge and therefore scale in lifelong learning, by accumulating knowledge and encapsulating multiple reusable skills into a single distilled network. The H-DRLN exhibits superior performance and lower learning sample complexity compared to the regular Deep Q Network (Mnih et. al. 2015) in sub-domains of Minecraft.

We present SAT encoding schemes for distance-based belief merging operators relying on the (possibly weighted) drastic distance or the Hamming distance between interpretations, and using sum, GMax (leximax) or GMin (leximin) as aggregation function. In order to evaluate these encoding schemes, we generated benchmarks of a time-tabling problem and translated them into belief merging instances. Then, taking advantage of these schemes, we compiled the merged bases of the resulting instances into query-equivalent CNF formulae. Experiments have shown the benefits which can be gained by considering the SAT encoding schemes we pointed out. Especially, thanks to them, we succeeded in computing query-equivalent formulae for merging instances based on hundreds of variables, which are out of reach of previous implementations.

Generation and evaluation of crowdsourced content is commonly treated as two separate processes, performed at different times and by two distinct groups of people: content creators and content assessors. As a result, most crowdsourcing tasks follow this template: one group of workers generates content and another group of workers evaluates it. In an educational setting, for example, content creators are traditionally students that submit open-response answers to assignments (e.g., a short answer, a circuit diagram, or a formula) and content assessors are instructors that grade these submissions. Despite the considerable success of peer-grading in massive open online courses (MOOCs), the process of test-taking and grading are still treated as two distinct tasks which typically occur at different times, and require an additional overhead of grader training and incentivization. Inspired by this problem in the context of education, we propose a general crowdsourcing framework that fuses open-response test-taking (content generation) and assessment into a single, streamlined process that appears to students in the form of an explicit test, but where everyone also acts as an implicit grader. The advantages offered by our framework include: a common incentive mechanism for both the creation and evaluation of content, and a probabilistic model that jointly models the processes of contribution and evaluation, facilitating efficient estimation of the quality of the contributions and the competency of the contributors. We demonstrate the effectiveness and limits of our framework via simulations and a real-world user study.

Nowadays, several crowdsourcing projects exploit social choice methods for computing an aggregate ranking of alternatives given individual rankings provided by workers. Motivated by such systems, we consider a setting where each worker is asked to rank a fixed (small) number of alternatives and, then, a positional scoring rule is used to compute the aggregate ranking. Among the apparently infinite such rules, what is the best one to use? To answer this question, we assume that we have partial access to an underlying true ranking. Then, the important optimization problem to be solved is to compute the positional scoring rule whose outcome, when applied to the profile of individual rankings, is as close as possible to the part of the underlying true ranking we know. We study this fundamental problem from a theoretical point of view and present positive and negative complexity results. Furthermore, we complement our theoretical findings with experiments on real-world and synthetic data.

I discuss here off-the-beaten-path beautiful, even spectacular results from number theory: not just about prime numbers, but also about related problems such as integers that are sum of two squares. The connection between these numbers and prime numbers will appear later in this article. A few important unsolved mathematical conjectures are presented in a unified approach, and some new research material is also introduced, especially an attempt at generalizing and unifying concepts related to data set density and limiting distributions. The approach is very applied, focusing on algorithms, simulations, and big data, to help discover fascinating results. Even though some of the most exciting topics of mathematics are discussed here (including fundamental, century-old problems still unresolved as well as brand new hypotheses), most of the article can be understood by the layman. Among other things, you will learn some new ways to estimate Pi based on non-traditional experiments, or how a conjecture for prime numbers somehow generalizes to apply to Fibonacci numbers as well.

These videos are part of an online course, Intro to Machine Learning. Check out the course here: https://www.udacity.com/course/ud120. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002. These videos are part of an online course, Intro to Machine Learning.

I teach one of the world's most popular MOOCs (massive online open courses), "Learning How to Learn," with neuroscientist Terrence J. Sejnowski, the Francis Crick Professor at the Salk Institute for Biological Studies. The course draws on neuroscience, cognitive psychology, and education to explain how our brains absorb and process information, so we can all be better students. Since it launched on the website Coursera in August of 2014, nearly 1 million students from over 200 countries have enrolled in our class. We've had cardiologists, engineers, lawyers, linguists, 12-year-olds, and war refugees in Sudan take the course. We get emails like this one that recently arrived: "I'll keep it short. I've recently completed your MOOC and it has already changed my life in ways you cannot imagine. I just turned 29, am in the middle of a career change to computer science, and I've never been more excited to learn."

Apache Spark is a lightning-fast framework for distributed computing that combines speed, scalability, in-memory processing, and fault tolerance with sophisticated analytics – perfect for dealing with massive datasets. This eBook takes you on a tour of Spark's powerful API; helps you create your first Spark program in Scala, Java, and Python; and gives detailed examples of real-world machine learning models from recommender systems to dimensionality reduction. You'll also learn about advanced topics like working with online machine learning and model evaluation methods using Spark Streaming. This eBook is free for today only so don't miss out!

The platform is providing a holistic online solution for teachers, students, and parents. Over two decades ago, Educomp set out to change the entire education system in the country. Since then, it claims to have empowered over 30 million learners and educators across over 65,000 schools. While Educomp was continuing to overhaul the education ecosystem through its smart class programmes, the top leadership in the company realised that they needed to take education beyond the conventional classrooms. Now, instead of taking students to classrooms, they had to flip the normal course and take classrooms to students, beyond boundaries.