The Presidency School of Computer Science and Engineering organised a seminar on “Transition from Classical Machine Learning to Deep Learning Architectures” on 17 March 2026 at 11:00 AM in Seminar Hall–2. The primary objective of the event was to provide participants with an understanding of the evolution of artificial intelligence, focusing on the shift from traditional machine learning techniques to advanced deep learning models.

 

The seminar commenced with a welcome address, followed by the introduction of the resource person, who was honoured with a plant sapling as a gesture of appreciation and sustainability. The session covered key aspects of machine learning and deep learning, supported by real-world applications and case studies. The event witnessed active participation from second-year CSE students of 4CSE03 and 4CSE04, along with faculty members.

 

The seminar provided participants with a clear understanding of the transition from classical machine learning approaches to modern deep learning architectures. Students gained insights into how traditional models function, their limitations, and how deep learning overcomes these challenges through hierarchical feature learning and large-scale data processing. The session enhanced participants’ knowledge of advanced architectures such as CNNs, RNNs, and Transformers, along with their applications in areas like image recognition, speech processing, and natural language understanding. It also emphasised the importance of data, computational power, and model optimisation in building intelligent systems.

 

Overall, the seminar strengthened technical knowledge, improved research orientation, and inspired students to engage in innovative work in artificial intelligence and deep learning, contributing to their academic and professional growth.