#2 Image Classification with KNN

In this lecture, we dive into one of the core tasks of Computer Vision – Image Classification. We explore the challenges behind teaching computers to recognize objects in images and discuss two foundational data-driven approaches: 🔹 K-Nearest Neighbors (KNN) – a simple yet powerful non-parametric method What you’ll learn in this lecture: ✔️ The semantic gap between raw image data and meaningful interpretation ✔️ Challenges in image classification (viewpoint, illumination, clutter, occlusion, etc.) ✔️ How Nearest Neighbor and KNN classifiers work ✔️ Choosing the right hyperparameters using validation sets & cross-validation This session builds the foundation for more advanced machine learning and deep learning methods in computer vision. 👨‍🏫 Instructor: Ranjeet Ranjan Jha 📂 Department of Mathematics --- ✨ If you found this lecture helpful, don’t forget to Like, Share, and Subscribe for more content on Machine Learning & Computer Vision. #MachineLearning #ComputerVision #KNN #DeepLearning