OctWave 3.0 | Workshop 1: Introduction to Machine Learning

Description

The first workshop, titled "Introducing to Machine Learning", was successfully conducted virtually via the Zoom platform on 06th July 2026 from 6.00 pm to 7.30 pm, with the active participation of 50 attendees. The session was led by Mr. Navindu De Silva, a PhD student at the School of Computing, National University of Singapore (NUS). The session commenced sharply at 6.00 pm, with the moderator welcoming all participants. Following this, the moderator introduced the guest speaker, highlighting his academic background and research contributions in the field of computing and artificial intelligence at NUS. This introductory segment concluded by 6.05 pm, setting the stage for an engaging and informative session. During the main presentation from 6.05 pm to 7.00 pm, Mr. De Silva conducted an in depth session on the foundational concepts of Machine Learning. He began by explaining the core definitions of machine learning and how it differs from traditional programming, followed by an overview of fundamental paradigms such as supervised, unsupervised, and reinforcement learning. He then proceeded to discuss the real world applications of ML across various industries, demystifying how data is used to train algorithms and make predictive models. Using accessible examples, he demonstrated the step by step workflow of a typical machine learning project, from data collection and preparation to model training and evaluation. Participants were encouraged to think critically about data structures, algorithmic logic, and the growing importance of artificial intelligence in modern technology. A Q&A session was held from 7.00 pm to 7.20 pm, during which participants raised insightful questions about getting started in machine learning, academic research pathways, and foundational software tools. Mr. De Silva addressed these queries comprehensively, offering practical advice and sharing valuable resources for further learning. At 7.20 pm, the token of appreciation was presented to the guest speaker by the organizing committee, acknowledging his time, effort, and valuable contribution to the success of the workshop. The vote of thanks was delivered, appreciating both the speaker and the 50 participants for their active engagement throughout the session. Finally, the workshop concluded at 7.30 pm. Overall, the workshop was a highly interactive and enlightening experience that provided participants with a solid foundational understanding of Machine Learning and inspired them to explore data-driven technologies further.