Meetings/Workshops on Information & Knowledge Management, Big Data Computing in the United States (USA)

About Apache Storm Training: Apache Storm is an open-source and distributed stream processing computation framework used for processing large volumes of high-velocity data.and its comparison with hadoop, Big Data world., etc.This Apache StormTraining from MindMajix will give you a working knowledge of the open-source Storm computational engine. You will be able to do distributed real-time data processing and come up with valuable insights.

Mindmajix Technologies INC;     Phone: [9174568403];     Email: info@mindmajix.com

ApacheStromTraining, Apachestormonline, stormtechnology

Algorithms,    Training in Computer Science, Courses

The ADT 2019 conference focus is on algorithmic decision theory broadly defined, seeking to bring together researchers and practitioners coming from diverse areas of Computer Science, Economics and Operations Research in order to improve the theory and practice of modern decision support.

Algorithms, Argumentation Theory, Artificial Intelligence, Computational Social Choice, Database Systems, Decision Analysis, Discrete Mathematics, Game Theory, Machine Learning, Matching, Multi-agent Systems, Multiple Criteria Decision Aiding, Networks, Optimization, Risk Management, and Utility Theory

Operational research,    Neural Networks and Artificial Intelligence, Machine Learning

Networks, Internet,    Search Engines and Data Mining

The advancement in computing and storage capabilities of modern computational clusters fosters use of novel statistical techniques in machine learning and deep networks. Such data-driven techniques allow one to learn model features and characteristics that are difficult for mathematical methods alone to reveal. Many computational methods achieve model and complexity discovery using methods that lie at the nexus of mathematical, statistical, and computational disciplines. Statistical methods often employ "big data" approaches that glean predictive capability from diverse and enormous databases of information. Emerging methods in machine learning and deep networks can provide impressive results. This workshop gathers researchers at the frontier of large-scale statistical computation, data science, tensor decompositions and approximations, and data-driven model learning, to focus on modern challenges that use data to reduce complexity of models.