The study focuses on linguistic aspects such as word choice for machine translation, parts of speech-tagging and word-sense disambiguation. The study's research paper considers the language learning process as a disambiguation problem and applies the linear separator technique. A formal definition of the disambiguation problem is defined in terms such as different word predicates, their classifications and features for the learning problem. In addition, various disambiguation methods are also emphasised for using them as linear separators.

As Data Science (DS) continues to be a growing field with promising prospects [1]-[3], it is attracting significant attention from many including learners of different learning backgrounds and applications areas. From a DS educator's perspective, the result is a very diverse cohort of learners. This typically includes (in no order) mathematicians, statisticians, operations researchers, computer scientists of all their colours, other scientists (e.g.

We introduce MeSys, a meaning-based approach, for solving English math word problems (MWPs) via understanding and reasoning in this paper. It first analyzes the text, transforms both body and question parts into their corresponding logic forms, and then performs inference on them. The associated context of each quantity is represented with proposed role-tags (e.g., nsubj, verb, etc.), which provides the flexibility for annotating an extracted math quantity with its associated context information (i.e., the physical meaning of this quantity). Statistical models are proposed to select the operator and operands. A noisy dataset is designed to assess if a solver solves MWPs mainly via understanding or mechanical pattern matching. Experimental results show that our approach outperforms existing systems on both benchmark datasets and the noisy dataset, which demonstrates that the proposed approach understands the meaning of each quantity in the text more.

Multi-source transfer learning has been proven effective when within-target labeled data is scarce. Previous work focuses primarily on exploiting domain similarities and assumes that source domains are richly or at least comparably labeled. While this strong assumption is never true in practice, this paper relaxes it and addresses challenges related to sources with diverse labeling volume and diverse reliability. The first challenge is combining domain similarity and source reliability by proposing a new transfer learning method that utilizes both source-target similarities and inter-source relationships. The second challenge involves pool-based active learning where the oracle is only available in source domains, resulting in an integrated active transfer learning framework that incorporates distribution matching and uncertainty sampling. Extensive experiments on synthetic and two real-world datasets clearly demonstrate the superiority of our proposed methods over several baselines including state-of-the-art transfer learning methods.

Self-paced learning (SPL) mimics the cognitive process of humans, who generally learn from easy samples to hard ones. One key issue in SPL is the training process required for each instance weight depends on the other samples and thus cannot easily be run in a distributed manner in a large-scale dataset. In this paper, we reformulate the self-paced learning problem into a distributed setting and propose a novel Distributed Self-Paced Learning method (DSPL) to handle large-scale datasets. Specifically, both the model and instance weights can be optimized in parallel for each batch based on a consensus alternating direction method of multipliers. We also prove the convergence of our algorithm under mild conditions. Extensive experiments on both synthetic and real datasets demonstrate that our approach is superior to those of existing methods.

Recommender systems can be formulated as a matrix completion problem, predicting ratings from user and item parameter vectors. Optimizing these parameters by subsampling data becomes difficult as the number of users and items grows. We develop a novel approach to generate all latent variables on demand from the ratings matrix itself and a fixed pool of parameters. We estimate missing ratings using chains of evidence that link them to a small set of prototypical users and items. Our model automatically addresses the cold-start and online learning problems by combining information across both users and items. We investigate the scaling behavior of this model, and demonstrate competitive results with respect to current matrix factorization techniques in terms of accuracy and convergence speed.

Forty percent of HR leaders believe artificial intelligence will help fill the skills gap. That's according to a new study by Learning House and Future Workplace, which surveyed 600 U.S. HR leaders. More than half of those surveyed acknowledged the skills gap and more than a third believe it's harder to fill open positions now than it was in 2017, but some critics say companies are not doing much to fix the problem. The study found that 74 percent of companies are only investing $500 per employee on learning and development. Jeremy Walsh, senior vice president of enterprise learning solutions at Learning House, said he was shocked by the low amount of money being spent on L&D.

It covers both the theoretical aspects of Statisticalconcepts and the practical implementation using R. Real life examples: Every concept is explained with the help of examples, case studies and source code in R wherever necessary. The examples cover a wide array of topics and range from A/B testing in an Internet company context to the Capital Asset Pricing Model in a quant finance context. What you will learn Harness R and R packages to read, process and visualize data Understand linear regression and use it confidently to build models Understand the intricacies of all the different data structures in R Use Linear regression in R to overcome the difficulties of LINEST() in Excel Draw inferences from data and support them using tests of significance Use descriptive statistics to perform a quick study of some data and present results Click here To join us for more information, get in touch keep enhancing Complete iOS 11 Machine Learning Masterclass 3. If you want to learn how to start building professional, career-boosting mobile apps and use Machine Learning to take things to the next level, then this course is for you. The Complete iOS Machine Learning Masterclass is the only course that you need for machine learning on iOS. Machine Learning is a fast-growing field that is revolutionizing many industries with tech giants like Google and IBM taking the lead. In this course, you'll use the most cutting-edge iOS Machine Learning technology stacks to add a layer of intelligence and polish to your mobile apps. We're approaching a new era where only apps and games that are considered "smart" will survive.

As scientific datasets increase in both size and complexity, the ability to label, filter and search this deluge of information has become a laborious, time-consuming and sometimes impossible task, without the help of automated tools enabled by machine learning. With this in mind, a team of researchers from the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) and UC Berkeley are developing innovative machine learning tools to pull contextual information from scientific datasets and automatically generate metadata tags for each file. Scientists can then search these files via a web-based search engine for scientific data, called Science Search, that the Berkeley team is building. As a proof-of-concept, the team is working with staff at Berkeley Lab's Molecular Foundry, to demonstrate the concepts of Science Search on the images captured by the facility's instruments. A beta version of the platform has been made available to Foundry researchers.

Today, almost every other industry implements artificial intelligence (AI). From Facebook suggesting new friends to computers trading stocks and even cars that park themselves, every realm of human life is impacted by AI. Among them, one of the sectors where AI is making promising strides is education. While there is yet some time before humanoid robots start teaching in classrooms, several other AI tools have already made their way to help teachers and students--shaping and redefining the educational experience of the future. One of the ways AI has impacted education is through application of higher levels of individualized learning.