This talk presents my learnings and findings on computational techniques for detecting, recognizing and predicting human emotions. While the recognition of emotional states come naturally to humans, these tasks pose several challenges to computational routines. The content describes how emotions can be interpreted from several channels, i.e: multimodal analysis, through facial expressions, body gestures, gait, speech patterns and text.
Workshops and Tutorials
2. Demonstrating Machine Learning Techniques on predicting financial stock prices
Jun 2021 | Conducted a tutorial session as part of teaching the Artifical Intelligence Literacy Competency Program in National University of Singapore
The Artifical Intelligence Competency program is designed to equip executive and administrative staff with practical AI knowledge in structuring projects with the Crisp-DM framework and this tutorial was delivered as part of my teaching workload. It details data ingestion via making API calls, exploratory analysis and the application of supervised machine learning techniques.
Online Asian Machine Learning School (OAMLS) is part of the highly regarded Asian Conference for Machine Learning for academics and researchers. I was a motivated participant who had an invaluable opportunity to network with academic experts from AI/ML field and present my work around affective computing in the form of a poster and video session. This is an extension to my research paper that was co-authored with a fellow academic peer and it is currently pending submissoin to the ACML journal.
This details my work and experiments that includes both a statistical component as well as ml/dl techniques on the impact music has on invoking emotions in humans.