A student chapter built around AI
DJS ACM SIGAI is the official student chapter for Artificial Intelligence and Machine Learning at SVKM's Dwarkadas J. Sanghvi College of Engineering.
DJS ACM SIGAI is the official student chapter for Artificial Intelligence and Machine Learning at SVKM's Dwarkadas J. Sanghvi College of Engineering.
Affiliated with the Association for Computing Machinery (ACM), SIGAI brings students together to learn, explore, and engage with artificial intelligence beyond the classroom. We organize seminars, workshops, hackathons, and other technical events that connect foundational concepts with current developments in AI.
Our activities range from mathematical foundations and machine learning fundamentals to neural architectures and generative AI, helping students understand both the foundations and the latest developments in the field.
DJS ACM SIGAI
Special Interest Group on Artificial Intelligence
AI & ML
Department of Artificial Intelligence & Machine Learning
ACM Global
Association for Computing Machinery, Chapter #188916
DJSCE Mumbai
SVKM's Dwarkadas J. Sanghvi College of Engineering
Knowledge, skills, and a community
We organize seminars, workshops, challenges, and conversations that help students learn AI beyond the classroom and connect with their peers.
First-Principles Seminars
Technical sessions that break down AI and machine learning concepts from the fundamentals, helping students build understanding rather than simply use tools.
Research & Emerging AI
Sessions and discussions that introduce students to research papers, emerging architectures, generative AI, and ideas shaping the field.
Hackathons & Challenges
Hackathons, technical challenges, and campus events that give students opportunities to apply their knowledge, solve problems, and collaborate under real constraints.
A Student Community
A space where students can meet peers, speakers, mentors, and fellow learners, exchange ideas, discover opportunities, and grow together.
Domains we explore
Core strands that shape our curriculum, workshop syllabus, and technical discussions.
Artificial Intelligence
AIFoundational AI concepts and techniques that shape intelligent systems across computing.
Machine Learning
MLMethods that allow systems to learn patterns from data and improve through experience.
Deep Learning
DLNeural approaches for learning complex representations across vision, language, and other domains.
Neural Networks
NEURAL NETThe architectures behind many modern AI systems, from basic feedforward networks to deeper models.
Transformers
TRANSFORMERArchitectures that have reshaped language, vision, and generative AI.
Backpropagation
∂L/∂WThe fundamental mechanism used to train neural networks by learning how errors flow through a model.