Deep Learning for Computer Vision

ABOUT THE COURSE : The automatic analysis and understanding of images and videos, a field called Computer Vision, occupies significant importance in applications including security, healthcare, entertainment, mobility, etc. The recent success of deep learning methods has revolutionized the field of computer vision, making new developments increasingly closer to deployment that benefits end users. This course will introduce the students briefly to traditional computer vision topics, before presenting deep learning methods for computer vision. This course delves into the fundamental concepts of neural networks, explores convolutional architectures, and covers the latest advancements in computer vision tasks such as image classification, object detection, segmentation, and generative modeling. Students will engage in hands-on programming assignments, as well as learn to implement and optimize deep learning models. By the end of the course, participants will be equipped with the skills and knowledge to contribute to the rapidly evolving field of computer vision, pushing the boundaries of what machines can perceive and understand. The course assumes a basic background in machine learning, and may be most useful to students that have also completed basic introductory materials on deep learning. INTENDED AUDIENCE : Senior undergraduate students, Post-graduate students, Industry professionals seeking to understand computer vision PREREQUISITES : Completion of a basic course in Machine Learning (Recommended, not mandatory) Completion of a course in Deep Learning, or exposure to topics in neural networks Knowledge of basics in probability, linear algebra, and calculus Experience of programming in Python If you are unsure whether you meet the background requirements for the course, please look at Assignment 0 (both theory and programming). If you are comfortable solving/following these assignments, you are ready for the course. INDUSTRIES SUPPORT : Many organization…