This module introduces students to Multilayer Perceptron (MLP) and Convolution Neural Network (CNN) Models with a comprehensive understanding of the architecture, training, and applications of MLPs and CNNs in the field of AI and machine learning. Through theoretical lectures, practical exercises, and hands-on implementation, students will gain the necessary knowledge and skills to design, train, and utilize MLP and CNN models for various tasks.

This module introduces the students to advanced topics/techniques in Convolutional Neural Networks (CNN) with an in-depth understanding of advanced techniques and applications in the field of CNNs. Focusing on topics such as transfer learning, layer visualization, and generative models, students will gain the knowledge and skills to leverage the power of CNNs for complex image analysis tasks.

This module on Object Detection and Semantic Segmentation using Deep Learning will provide students with a comprehensive understanding of advanced techniques for detecting objects and performing pixel-level segmentation in images and videos. Through a combination of theoretical lectures, practical exercises, and hands-on projects, students will gain the necessary knowledge and skills to effectively tackle complex computer vision tasks using deep learning methods.

This module on Deep Learning for Computer Vision with PyTorch provides students with a comprehensive understanding of using the PyTorch framework to solve various computer vision tasks. Through a combination of hands-on exercises, and practical projects, students will gain the necessary knowledge and skills to effectively tackle classification, generative modeling, object detection, and image segmentation tasks using deep learning techniques.

This module on Image Segmentation provides students with a comprehensive understanding of advanced techniques for segmenting and analyzing images using deep learning methods. Students will gain the necessary knowledge and skills to build, train, and evaluate image segmentation models for a range of computer vision applications.

This module serves as a culminating experience in which students will apply concepts and techniques from across the course in a practical computer vision context. Through project-based work and applied problem-solving, students will further develop their understanding of deep learning with PyTorch while demonstrating their ability to approach real-world computer vision tasks.