Object Detection
Object detection involves localizing and classifying multiple objects within an image. In this subheading, students explore algorithms such as region-based convolutional neural networks (R-CNN), You Only Look Once (YOLO), and Single Shot MultiBox Detector (SSD). They understand the challenges associated with object detection, such as scale variation, occlusion, and viewpoint changes. Through practical exercises, students gain proficiency in building object detection models and applying them to tasks such as pedestrian detection, vehicle detection, and object tracking.
Image Segmentation
Image segmentation involves dividing an image into meaningful regions or segments. In this subheading, students learn about techniques such as semantic segmentation and instance segmentation. They explore algorithms like Fully Convolutional Networks (FCN), U-Net, and Mask R-CNN. Students gain hands-on experience in building image segmentation models and applying them to tasks such as medical image analysis, autonomous driving, and image editing.
AI in Healthcare
This session explores the role of AI and ML in healthcare, revolutionizing patient care, diagnostics, and research. Students gain insights into the applications of AI in medical imaging analysis, disease diagnosis, personalized medicine, and healthcare management.
