A State-Level Workshop on “Teaching Machine Learning – Approaches & Pedagogy” was successfully conducted on 13 December 202 by the Department of Computer Applications, Don Bosco College, Panjim with the objective of deliberating effective strategies for teaching Machine Learning, with due emphasis on both theoretical foundations and practical implementation. The workshop witnessed active participation from 11 faculty members from 10 institutions across Goa providing a valuable platform for academic interaction and curriculum-oriented discussions.
The workshop commenced with an inaugural ceremony. The BCA Department Coordinator, Mr Rohan R Kerkar delivered the welcome note, setting the context and objectives of the workshop. This was followed by the formal introduction of the subject experts by Assistant Professor, Mr. Shantanu Arlekar. The subject experts, Mr. Baskar Sundarrajan, Associate Professor, Computer Science Discipline, Goa Business School, Goa University, and Dr. Shreyas Simu, Associate Professor, Electronics & Computer Science Department, Don Bosco College of Engineering, Fatorda, Margao, Goa, were felicitated by the College Director Rev. Fr. Kinley D’Cruz and the Principal, Prof. Teotone Vaz. The Director and the Principal addressed the gathering, highlighting the relevance of Machine Learning in contemporary education and research. The inaugural session concluded with a formal vote of thanks by Assistant Professor, Ms Neeke Golatkar , after which the technical sessions commenced.


The workshop proved to be highly insightful and constructive, fostering a shared understanding of innovative pedagogical approaches in Machine Learning education. The expert inputs, collaborative discussions, and structured division of academic responsibilities are expected to significantly enhance the quality and uniformity of teaching across institutions. The workshop successfully achieved its objective of strengthening faculty preparedness and aligning Machine Learning pedagogy with current academic and industry expectations.
The workshop proved to be highly insightful and constructive, fostering a shared understanding of innovative pedagogical approaches in Machine Learning education. The expert inputs, collaborative discussions, and structured division of academic responsibilities are expected to significantly enhance the quality and uniformity of teaching across institutions. The workshop successfully achieved its objective of strengthening faculty preparedness and aligning Machine Learning pedagogy with current academic and industry expectations.