Danna Gurari

Computer vision is the subdiscipline of artificial intelligence focused on creating computers that can ‘see’. In this course, students will learn about core and new problems in this field through examination of the types of algorithms commonly used as well as the data employed to train and evaluate those algorithms. The course is taught in a seminar style, with students expected to regularly read and critique research papers from premiere computer vision conferences (such as CVPR, ICCV, and ECCV). As a result, students will also learn how to engage with the latest research in computer vision.

This course will cover machine learning approaches that are based on neural networks, also referred to as ‘deep learning’, and how these approaches are applied to solve artificial intelligence problems.

Computer vision is the subdiscipline of artificial intelligence focused on creating computers that can ‘see’. In this course, students will learn about core and new problems in this field through examination of the types of algorithms commonly used as well as the data employed to train and evaluate those algorithms. The course is taught in a seminar style, with students expected to regularly read and critique research papers from premiere computer vision conferences (such as CVPR, ICCV, and ECCV). As a result, students will also learn how to engage with the latest research in computer vision.

This class will cover core and cutting edge concepts employed in machine learning to solve artificial intelligence problems. Students will learn the theory behind a range of machine learning tools and practice applying the tools to, for example, textual data (natural language processing), visual data (computer vision), and the combination of both textual and visual data.

This class will cover fundamental and state-of-art problems in computer vision, the sub-discipline of artificial intelligence that tries to create computers that can “see”. Students will explore this field through examination of the human-based challenges faced when teaching computers to see.