The Presidency School of Computer Science & Engineering (PSOCSE) organised a five-day Faculty Development Programme (FDP) on “Machine Learning Techniques” from 21 to 25 July 2026, from 9:00 am to 4:15 pm at LFL01. Convened by Dr Pallavi R and coordinated by Ms Shet Reshma Prakash and Ms Prachi Amol Gadhikar, the offline programme brought together 51 registered faculty members. The FDP was designed to strengthen participants’ knowledge and teaching competencies in Machine Learning and Artificial Intelligence through expert-led sessions, interactive lectures, live demonstrations, hands-on laboratory activities, case studies and discussions.

The programme featured resource persons from academia and industry, including Mr Bhavya Verma, Scientist, CMTI, Bengaluru; Dr Deepti Sisodhia, Associate Professor, MIT, Bengaluru; Dr Swati Sharma; Dr Anand Prakash; Dr Sandeep Albert Mathias; Dr Bhuvaneshwari Patil; Dr Megala G; and Dr Sivaramakrishnan S from Presidency University, Bengaluru. Key topics included machine learning fundamentals and industrial applications, the transition from classical machine learning to deep learning, model evaluation metrics, quantum machine learning, supervised and unsupervised learning, ensemble learning, imbalanced machine learning and data balancing techniques. A hands-on session on credit card fraud detection using Google Colab provided participants with practical exposure to real-world machine learning applications.

The FDP enhanced participants’ understanding of both foundational and advanced Machine Learning concepts while strengthening their practical implementation skills. The programme encouraged knowledge sharing, interdisciplinary collaboration and engagement with industry-oriented applications, with the learning outcomes supporting improved teaching practices, curriculum enhancement, research in AI and Machine Learning, and the development of industry-relevant academic activities. Overall, the FDP contributed to strengthening faculty expertise and creating opportunities for future academic, research and industry collaborations.