Meta-algorithms in Machine Learning
…Ensemble methods; models that help win competitions; Bagging, Boosting, Stacking…
Speaker:
Chapter Chair, Book Author, and SJSU Faculty Member,
Vishnu S. Pendyala, PhD
Date: May 31, 2022, 6:00 PM (PST)
Virtual Event via Zoom and YouTube live
Synopsis:
The word ‘meta’ indicates something beyond, a level up, or a higher layer. Meta-algorithms in Machine learning work on top of the known classification and regression algorithms such as Decision Trees, Logistic Regression, and Support Vector Machines to improve the performance substantially. It is often observed that these algorithms fetch top positions in the competition leaderboards and are now commonly used in the industry as well. This talk will cover some of the popular techniques of Meta-learning and explain why they generally work well. The techniques covered will include bagging, boosting, stacking, and algorithms within those broad categories such as random forest, adaboost, and gradient boosting. Time permitting, related concepts such as one-shot / few-shot learning, siamese neural network, and transfer learning will also be introduced.
Speaker Biography:
Dr. Vishnu S. Pendyala is a faculty member of the Department of Applied Data Science at San Jose State University and is the Chair of the IEEE Computer Society, Silicon Valley Chapter. He has over two decades of experience in the software industry and recently completed his 3 year term as an ACM Distinguished speaker. He holds MBA in Finance and PhD, MS, and BE degrees in Computer Engineering from US and Indian universities. Dr. Pendyala taught a one-week course sponsored by the Ministry of Human Resource Development (MHRD), Government of India, under the GIAN program in 2017 to Computer Science faculty from all over the country and delivered the keynote in a similar program sponsored by AICTE, Government of India in 2022.