The field of optimization and machine learning are increasingly interplayed and optimization in different problems leads to the use of machine learning approaches‎. ‎Machine learning algorithms work in reasonable computational time for specific classes of problems and have important role in extracting knowledge from large amount of data‎. ‎In this paper‎, ‎a methodology has been employed to opt...

Computer Vision (CV) and Machine Learning (ML) have seen a tremendous evolution within the last 15 years. One of the main drivers of this success is the application of machine learning methods to computer vision tasks (image registration, segmentation, 3D reconstruction, tracking, object detection, image classification, ...). These days it is widely agreed that difficult computational problems ...

Machine learning based system are increasingly being used for sensitive tasks such as security surveillance, guiding autonomous vehicle, taking investment decisions, detecting and blocking network intrusion and malware etc. However, recent research has shown that machine learning models are venerable to attacks by adversaries at all phases of machine learning (e.g., training data collection, tr...

In this paper, we argue for the adoption of a normative definition of fairness within the machine learning community. After characterizing this definition, we review the current literature of Fair ML in light of its implications. We end by suggesting ways to incorporate a broader community and generate further debate around how to decide what is fair in ML.

Machine Learning (ML) is one of the most exciting and dynamic areas of modern research and application. The purpose of this review is to provide an introduction to the core concepts and tools of machine learning in a manner easily understood and intuitive to physicists. The review begins by covering fundamental concepts in ML and modern statistics such as the bias-variance tradeoff, overfitting...

Owing to the predominant role of Machine Learning(ML) across domains, it is being introduced at multiple levels education, including K-12. Researchers have leveraged games, augmented reality and other ways make learning ML concepts interesting. However, most existing games teach either focus on use-cases applications instead core or directly introduce terminologies, which might be overwhelming ...

Machine learning (ML) is believed to be an effective and efficient tool to build reliable prediction model or extract useful structure from an avalanche of data. However, ML is also criticized by its difficulty in interpretation and complicated parameter tuning. In contrast, visualization is able to well organize and visually encode the entangled information in data and guild audiences to simpl...

in this work, several machine learning techniques are presented for nanofiltration modeling. according to the results, specific errors are defined. the rejection due to nanofiltration increases with pressure but decreases with increasing the concentration of chloride ion. methods of machine learning represent the rejection of nanofiltration as a function of concentration, ph, pressure and also ...