Virtual and Augmented Reality
This course presents an introduction to virtual and augmented reality technologies, with an emphasis on designing and developing interactive virtual and augmented reality experiences. The course will cover the history of the area, fundamental theory, interaction techniques, and specific application areas. Concepts from the contributing fields of computer vision, computer graphics and human computer interaction will be introduced in the context of virtual and augmented reality. Students will be tasked with creating their own virtual or augmented reality application as a course project.
When offered: Fall
Instructor: Harald Haraldsson
Topics in Mixed Reality
This course explores the field of mixed reality through research topics at the intersection of computer vision, computer graphics, human-computer interaction. Topics covered may include but not limited to: 3D interaction techniques, remote collaboration, tracking methods, photometric registration, navigation and more.
When offered: Spring
Instructor: Harald Haraldsson
Introduction to Computer Vision
The goal of computer vision is to compute properties of the three-dimensional world from digital images. Problems in this field include reconstructing the 3D shape of an environment, determining how things are moving, and recognizing people and objects and their activities, all through analysis of images and videos.
This course will provide an introduction to computer vision, with topics including image formation, feature detection, motion estimation, image mosaics, 3D shape reconstruction, object/face detection and recognition, and deep learning.
Applications of these techniques include building 3D maps, creating virtual characters, organizing photo and video databases, human computer interaction, video surveillance, automatic vehicle navigation, robotics, virtual and augmented reality, medical imaging, and mobile computer vision.
When offered: Spring
Instructor: Noah Snavely
Applied Machine Learning
Learn and apply key concepts of modeling, analysis and validation from Machine Learning, Data Mining and Signal Processing to analyze and extract meaning from data. Implement algorithms and perform experiments on images, text, audio and mobile sensor measurements. Gain working knowledge of supervised and unsupervised techniques including classification, regression, clustering, feature selection, association rule mining and dimensionality reduction.
When offered: Fall
Instructor: Serge Belongie
HCI and Design
Human-Computer Interaction (HCI) and design theory and techniques. Methods for designing, prototyping, and evaluating user interfaces. Basics of visual design, graphic design, and interaction design. Understanding human capabilities, interface technology, interface design methods, prototyping tools, and interface evaluation tools and techniques.
When offered: Fall
Instructors: Shiri Azenkot, Nicki Dell