Machine Learning and Data Mining in the Age of Big Data
Machine Learning and Data Mining in the Age of Big Data (NYC)
With the transformation of our society into a “digital world,” machine learning has emerged as an essential approach to extracting useful information from large collections of data. However, challenges remain for using machine learning effectively, some of which, can be overcome using conceptual modeling. We examine a popular cross-industry standard process for data mining, commonly known as CRISP-DM Directions, and show the role of conceptual modeling at each stage of this process.
This exposition demonstrates the broad potential of conceptual modeling to advance machine learning, specifically by:
- Supporting the application of machine within organizations.
- Improving the usability of machine learning as decision tools (e.g., by making them more transparent).
- Optimizing the performance of machine learning algorithms.
Meet Our Speaker:
Arturo Castellanos is an Assistant Professor in the Zicklin School of Business at Baruch College (CUNY). His focus is on helping organizations implement data-driven strategies to create competitive advantage. His research interests are in systems analysis and design (UGC and data quality), machine learning, and blockchain. He teaches courses on Business Analytics, Data Warehousing, and Information Systems. He has a PhD in Information Systems from Florida International University.
We hope you can make it!
IIBA New York City Chapter
To register, click here.