Algorithms and hardware architectures for data intensive applications in machine & deep learning, statistical inference and pattern recognition

Presenter: 

Saeid Nooshbadi, Michigan Tech, USA

Abstract: 

The aim of the tutorial is to present the latest development in parallel algorithms and hardware architectures for data-intensive applications from the big data domains that include machine learning, numerical analysis and statistical inferences, time series analysis, and pattern recognition and classification and secure data processing.

The emphasis in the tutorial is on the exploration of the alternative computing paradigms that are needed to achieve realtime performance.