Instructor: Manmohan Chandraker
Email: mkchandraker [AT] eng [DOT] ucsd [DOT] edu
Lectures: WF 5-6:20pm in CSE 2154
Instructor office hours: F 2:30-3:30pm in CSE 4122
TAs: Shashank Shastry (firstname.lastname@example.org) and Zhengqin Li (email@example.com)
TA office hours: Tu noon to 1pm (Shashank, in CSE B240A) and Th 4-5pm (Zhengqin, in CSE 4127)
Computer vision is a branch of artificial intelligence that seeks to understand the world based on visual cues, primarily images. This understanding can be in the form of recovering three-dimensional scene properties, recognizing objects, labeling parts of the image into semantic categories, recognizing actions in videos or predicting behaviors.
The field of computer vision has made tremendous progress in recent years. It is increasingly becoming a part of our daily lives, with applications such as image search, social media or surveillance. There is also widespread acceptance that computer vision will play a large role in enabling technologies of the future, such as self-driving cars or smart homes. Computer vision seeks to analyze images to draw meaningful conclusions, which often requires drawing upon prior knowledge based on past observations. This makes machine learning a useful tool for computer vision. Indeed, the advent of deep learning frameworks has led to significant gains, allowing computer vision applications to succeed even in domains considered challenging just five years ago. In this class, we will explore the fundamentals of diverse topics in computer vision and understand how they are shaping the modern world of technology.
A background in linear algebra and calculus is required. Programming experience in Python or Matlab is required. Courses that cover these might be Math 20F, CSE 100 or Math 176, CSE 101 or Math 188. Students are encouraged to contact the instructor if unsure about meeting any criteria for enrollment.
The course will primarily involve lectures by the instructor. The goal of the course is to develop an understanding of the current state of computer vision and gain appreciation of its limits and potential. Active participation by students is encouraged for in-class discussions.
Students will be required to do a mini-project or write a critical analysis on a focused topic. Wide flexibility is available for the choice of topic, but should be discussed with the instructor beforehand. The best analyses or projects may consist of conceptual development, or a practical implementation that builds upon the topics discussed in class.
Students may take the class with a letter grade option for 4 units. Grades will be weighted as 30% for a final exam, 30% for homeworks, 30% for mini-project and 10% for in-class participation. There will be three homework assignments. The mini-project may be in groups of up to three students.