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T05: No Code Machine Learning and Data Analytics

Saturday, 29 June 2024, 13:30 - 17:30 EDT (Washington DC)
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Prof. Dvijesh Shastri (short bio)

University of Houston-Downtown, USA



The tutorial aims to introduce machine-learning concepts for data analytics and provide hands-on experience in developing machine-learning models without writing computer programs.


Content and benefits:

A foundational understanding of machine learning (ML) is crucial for constructing both descriptive and predictive ML models. Several pre-built ML libraries are available for widely-used programming languages such as R and Python, facilitating the development of ML models. Nevertheless, utilizing these libraries necessitates proficient programming skills, thereby limiting access to individuals familiar with Python or R programming.

The proposed no-code ML approach alleviates the programming requirement by providing tools for model building and evaluation through a visual programming approach. Specifically, the tutorial will provide hands-on training using Orange – a visual programming tool for creating machine-learning models. The ability to construct ML models without the necessity of coding would democratize the field of machine learning and expedite the data analytics process.


Target Audience:

Anyone interested in learning machine learning for data analytics.


Additional platform or tool(s) to be used by the tutors:

Orange (https://orangedatamining.com/)


List of materials or devices required by the participants:


Bio Sketch of Presenter:

Dr. Dvijesh Shastri is a Professor of Computer Science and an Assistant Chair of the Department of Computer Science and Engineering Technology at the University of Houston – Downtown. He specializes in affective computing and human-centered computing. He has developed many computer vision and image processing algorithms that capture physiological signals from thermal imagery and associate them with human psychological states; these algorithms offer a novel toolkit for human behavior analysis. He has developed a novel non-contact way of measuring emotional perspiration responses from the face, which is a completely different approach from the contact-based clinical standard of measuring perspiration from fingers. This method allows realistic psychophysiological studies where subjects can operate naturally. Dr. Shastri has co-authored more than thirty publications in conferences and journals. He has been PI and CO-PI for multiple grants. He received a B.E. in Electrical Engineering from Sardar Patel University in 1997, a M.S. in Computer Science from Wright State University in 2001, and a Ph.D. in Computer Science from the University of Houston in 2007.