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Data Science: Machine Learning and Statistical Modeling in R

@machinelearnbot

In this course, we will teach you advanced techniques in machine learning with the latest code in R. Now is the time to take control of your data and start producing superior statistical analysis with R. You will delve into statistical learning theory and supervised learning; design efficient algorithms; learn about creating Recommendation Engines; use multi-class classification and deep learning and more. This course starts with teaching you how to set up the R environment, which includes installing RStudio and R packages. This course aims to excite you with awesome projects focused on analysis, visualization, and machine learning. You will explore, in depth, topics such as data mining, classification, clustering, regression, predictive modeling, anomaly detection, and more.


From Data to Actions in Intelligent Transportation Systems: a Prescription of Functional Requirements for Model Actionability

arXiv.org Artificial Intelligence

Advances in Data Science are lately permeating every field of Transportation Science and Engineering, making it straightforward to imagine that developments in the transportation sector will be data-driven. Nowadays, Intelligent Transportation Systems (ITS) could be arguably approached as a "story" intensively producing and consuming large amounts of data. A diversity of sensing devices densely spread over the infrastructure, vehicles or the travelers' personal devices act as sources of data flows that are eventually fed to software running on automatic devices, actuators or control systems producing, in turn, complex information flows between users, traffic managers, data analysts, traffic modeling scientists, etc. These information flows provide enormous opportunities to improve model development and decision-making. The present work aims to describe how data, coming from diverse ITS sources, can be used to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes; in other words, for data-based models to fully become actionable. Grounded on this described data modeling pipeline for ITS, we define the characteristics, engineering requisites and challenges intrinsic to its three compounding stages, namely, data fusion, adaptive learning and model evaluation. We deliberately generalize model learning to be adaptive, since, in the core of our paper is the firm conviction that most learners will have to adapt to the everchanging phenomenon scenario underlying the majority of ITS applications. Finally, we provide a prospect of current research lines within the Data Science realm that can bring notable advances to data-based ITS modeling, which will eventually bridge the gap towards the practicality and actionability of such models.



How AI, AR, and Big Data Will Change the Future of Education - DZone AI

#artificialintelligence

Education has always been a hot topic among intellectuals and reformers. It has seen quite a change in the last decade or so, but not significant enough to get noticed. The new era of learning is still focused on keeping students in the classroom in the hopes that they will bring a better future to themselves and to society as a whole. The current education system has always been focused on a batch study where individual growth is never focused on. With the expansion of the internet, things have changed drastically, as now, anyone can do self-study using YouTube, Udacity, or TED.


Artificial Intelligence for Social Good: A Survey

arXiv.org Artificial Intelligence

Its impact is drastic and real: Youtube's AIdriven recommendation system would present sports videos for days if one happens to watch a live baseball game on the platform [1]; email writing becomes much faster with machine learning (ML) based auto-completion [2]; many businesses have adopted natural language processing based chatbots as part of their customer services [3]. AI has also greatly advanced human capabilities in complex decision-making processes ranging from determining how to allocate security resources to protect airports [4] to games such as poker [5] and Go [6]. All such tangible and stunning progress suggests that an "AI summer" is happening. As some put it, "AI is the new electricity" [7]. Meanwhile, in the past decade, an emerging theme in the AI research community is the so-called "AI for social good" (AI4SG): researchers aim at developing AI methods and tools to address problems at the societal level and improve the wellbeing of the society.