One of the important measures of quality of education is the performance of students in the academic settings. Nowadays, abundant data is stored in educational institutions about students which can help to discover insight on how students are learning and how to improve their performance ahead of time using data mining techniques. In this paper, we developed a student performance prediction model that predicts the performance of high school students for the next semester for five courses. We modeled our prediction system as a multi-label classification task and used support vector machine (SVM), Random Forest (RF), K-nearest Neighbors (KNN), and Mult-layer perceptron (MLP) as base-classifiers to train our model. We further improved the performance of the prediction model using state-of-the-art partitioning schemes to divide the label space into smaller spaces and use Label Powerset (LP) transformation method to transform each labelset into a multi-class classification task. The proposed model achieved better performance in terms of different evaluation metrics when compared to other multi-label learning tasks such as binary relevance and classifier chains.

Online learning algorithms are designed to solve prediction and learning problems for streaming data or batch data whose volume is too large to be processed all at once. Applications include online routing [1], online auctions [2], online classification and regression [3], as well as online resource allocation [4].

In order to facilitate the accesses of general users to knowledge graphs, an increasing effort is being exerted to construct graph-structured queries of given natural language questions. At the core of the construction is to deduce the structure of the target query and determine the vertices/edges which constitute the query. Existing query construction methods rely on question understanding and conventional graph-based algorithms which lead to inefficient and degraded performances facing complex natural language questions over knowledge graphs with large scales. In this paper, we focus on this problem and propose a novel framework standing on recent knowledge graph embedding techniques. Our framework first encodes the underlying knowledge graph into a low-dimensional embedding space by leveraging generalized local knowledge graphs. Given a natural language question, the learned embedding representations of the knowledge graph are utilized to compute the query structure and assemble vertices/edges into the target query. Extensive experiments were conducted on the benchmark dataset, and the results demonstrate that our framework outperforms state-of-the-art baseline models regarding effectiveness and efficiency.

Understanding narratives requires reading between the lines, which in turn, requires interpreting the likely causes and effects of events, even when they are not mentioned explicitly. In this paper, we introduce Cosmos QA, a large-scale dataset of 35,600 problems that require commonsense-based reading comprehension, formulated as multiple-choice questions. In stark contrast to most existing reading comprehension datasets where the questions focus on factual and literal understanding of the context paragraph, our dataset focuses on reading between the lines over a diverse collection of people's everyday narratives, asking such questions as "what might be the possible reason of ...?", or "what would have happened if ..." that require reasoning beyond the exact text spans in the context. To establish baseline performances on Cosmos QA, we experiment with several state-of-the-art neural architectures for reading comprehension, and also propose a new architecture that improves over the competitive baselines. Experimental results demonstrate a significant gap between machine (68.4%) and human performance (94%), pointing to avenues for future research on commonsense machine comprehension. Dataset, code and leaderboard is publicly available at https://wilburone.github.io/cosmos.

There is currently a lot of talk about automated machine learning. There is also a high level of skepticism. I am here with data scientists Paolo Tamagnini, Simon Schmid and Christian Dietz, to ask a few questions on this topic from their point of view and I found this concept of guided automation quite interesting as well, since it is directly involved in the practice of automated machine learning. Rosaria Silipo: What is automated machine learning? Christian Dietz: Automated machine learning is about building a system, process or application able to automatically create, train and test machine learning models with as little human input as possible.

Link: Machine Learning Basics - SQL Server 2017, R, Python & T-SQL This article explains the basics of SQL Server Machine Learning Services. Also you get to compare the functional equivalent of both languages with reference manuals available in this course. These examples range from basics to advanced complex visualizations. Machine Learning Basics with SQL Server 2017, R and Python is a course in which a student having no experience / awareness of Machine Learning / R / Python / SQL Server 2017 Machine Learning Services would be trained step by step to a level where the student is confident to independently work independently with each of them. Course includes practical hands-on queries with explanation and analysis, and theoretical coverage of key concepts.

Link: Deep Learning with TensorFlow 2.0 [2019] Data Science Deep Learning Machine-Learning Scientific Libraries ... Learn about the updates being made to TensorFlow in its 2.0 version. We'll give an ... 8,767 students enrolled Created by 365 Careers, 365 Careers Team Gain a Strong Understanding of TensorFlow - Google's Cutting-Edge Deep Learning Framework Build Deep Learning Algorithms from Scratch in Python Using NumPy and TensorFlow Set Yourself Apart with Hands-on Deep and Machine Learning Experience Grasp the Mathematics Behind Deep Learning Algorithms Understand Backpropagation, Stochastic Gradient Descent, Batching, Momentum, and Learning Rate Schedules Know the Ins and Outs of Underfitting, Overfitting, Training, Validation, Testing, Early Stopping, and Initialization Competently Carry Out Pre-Processing, Standardization, Normalization, and One-Hot Encoding Some basic Python programming skills You'll need to install Anaconda. We will show you how to do it in one of the first lectures of the course. All software and data used in the course are free. Data scientists, machine learning engineers, and AI researchers all have their own skillsets.

They were given six weeks to accomplish the task, and that is what they did. A couple of weeks ago, members of the Red River Rage, which is what the students from Fargo North, Fargo South and Davies High School call themselves, were among the winners of a regional tournament held in Grand Forks. Now, they are heading to the 2019 FIRST Robotics Championship tournament in Detroit, which will be held April 24-27. FIRST Robotics is an international high school robotics competition that gives students real-world engineering experience. There are about 4,800 teams around the world and about 600 will compete in Detroit, according to Ellen Shafer, one of several mentors of the local high school team, which includes her son, Ethan Shafer, a junior at North High.

Automation is not likely to eliminate millions of jobs any time soon--but the U.S. still needs vastly improved policies if Americans are to build better careers and share prosperity as technological changes occur, according to a new MIT report about the workplace. The report, which represents the initial findings of MIT's Task Force on the Work of the Future, punctures some conventional wisdom and builds a nuanced picture of the evolution of technology and jobs, the subject of much fraught public discussion. The likelihood of robots, automation, and artificial intelligence (AI) wiping out huge sectors of the workforce in the near future is exaggerated, the task force concludes--but there is reason for concern about the impact of new technology on the labor market. In recent decades, technology has contributed to the polarization of employment, disproportionately helping high-skilled professionals while reducing opportunities for many other workers, and new technologies could exacerbate this trend. Moreover, the report emphasizes, at a time of historic income inequality, a critical challenge is not necessarily a lack of jobs, but the low quality of many jobs and the resulting lack of viable careers for many people, particularly workers without college degrees.

Raytheon has deployed artificial intelligence technology to support training programs for the U.S. Army and envisions AI and machine learning tools integrated with virtual and augmented reality to help improve the skills of warfighters. The company said Wednesday it is developing intelligent bots designed to handle lower-level logistics functions at the Army's Multinational Readiness Center in Germany and intends to use data from the bots to provide training aids, gear equipment for the Army's virtual training programs. Malachi Lawson, technical director for global training solutions at Raytheon, said that VR and AR-driven environments integrated with AI result in less dangerous mission exercises compared to live training. Corey Hendricks, the company's senior solutions architect, noted that AI and ML capabilities can also be used to develop hyper-engagement tools that work to inform warfighters' decision-making by analyzing data such as routines and daily habits. He added that such emerging technologies may support efforts to improve soldiers' cultural awareness and help commanders in battlefield operations through the implementation of combat doctrine data.