Foundations of Computer Vision: Understand the fundamental concepts of computer vision, including the physics of image formation, camera models, and image representation. Explore key techniques in image processing such as filtering, edge detection, and color space transformations.

Neural Networks for Vision Tasks: Delve into the architecture and implementation of Convolutional Neural Networks (CNNs) for image classification, localization, and segmentation. Learn about various layers like convolutional, pooling, and fully connected layers, and understand how they work together to extract features from images.

Image Preprocessing Techniques: Gain proficiency in essential preprocessing techniques such as image normalization, augmentation, resizing, and cropping. Understand how these techniques help in enhancing the performance and generalization of computer vision models.

Object Detection and Recognition: Implement and train advanced models for detecting and recognizing objects in images and videos. Explore algorithms such as YOLO (You Only Look Once), SSD (Single Shot MultiBox Detector), and Faster R-CNN (Region-based Convolutional Neural Networks) to perform real-time object detection.

Advanced Computer Vision Algorithms: Dive deeper into specialized algorithms for complex tasks. Learn about instance segmentation with Mask R-CNN, semantic segmentation with U-Net, and generative models like GANs (Generative Adversarial Networks) for creating synthetic images.

Practical Implementation: Hands-on experience with popular libraries and frameworks such as OpenCV, TensorFlow, and Keras. Build, train, and deploy computer vision models on real-world datasets. Learn techniques for model optimization and evaluation, and understand how to deploy models on edge devices for real-time applications.