Machine Learning and Applications
Machine learning (ML), a spin-off of artificial intelligence, has become a de facto framework for research and competitive business data analysis. In fact, ML has become a necessity for data analysis in domains where data volume and complexity have surpassed practical use of traditional data analysis methods.The growing use of ML is due to a confluence of factors: increasing availability of large data set (big data), advances in computing and algorithmic model, data storage, and competitive pressures.
This talk presents the current state of ML and its companion, data mining at a pragmatic level. It samples available tools, algorithms and platforms for ML, and discusses considerations in creating generalizable data models. Also included in this talk, are illustrations of concepts through three case studies of increasing complexity.
D. Joachim has conducted research in both academic and industry environments, much of which involved some form of machine learning. His current research focus involves speech modeling for healthcare. Joachim holds a PhD in Electrical Engineering.
University of Massachusetts Boston
Science Hall Second Floor Room 62
100 William T. Morrissey Boulevard
Boston, MA 02125
Directions and parking information can be found at: http://www.umb.edu/parking_transport/directions.html
Date and time:
Saturday, March 30, 2019
12:45 PM—2:30 PM