Lecture 09: Batch Learning vs. Online Learning

Описание: This lecture delves into the fundamental concepts and distinctions between two primary machine learning paradigms: Batch Learning and Online Learning. We will embark on a journey through the realm of Batch Learning, where models are trained using the entirety of available data in comprehensive training sessions. This approach is contrasted with Online Learning, a dynamic methodology where models incrementally adapt by learning from new data points as they arrive, enabling real-time updates and adaptability to evolving data streams.

Throughout this session, participants will gain insights into the practical applications, advantages, and limitations of each learning paradigm, supported by real-world examples and case studies. We will explore the criteria for selecting the appropriate learning strategy based on specific project requirements, data availability, and the need for model adaptability over time.

Join us as we unravel the intricacies of these machine learning types, equipping you with the knowledge to make informed decisions on the most suitable approach for your AI-driven projects and research endeavors. This lecture is designed for students, researchers, and professionals keen on enhancing their understanding of machine learning's diverse landscapes.