The session delved into advanced techniques for identifying anomalies in complex datasets, emphasizing their role in improving the robustness and reliability of machine learning models. The session explores feature selection strategies to streamline datasets, reduce dimensionality, and improve computational efficiency while maintaining critical predictive insights. The students gained valuable insights into practical implementations, including algorithmic advancements and real-world applications of anomaly detection in fields such as fraud detection, network security, and predictive maintenance.
KPRIET – An AI Integrated Campus
Preparing future-ready engineers with AI-integrated teaching and learning. KPRIET integrates Artificial Intelligence across teaching, learning, research and innovation to create a smarter, future-ready campus experience for students and faculty.