Computer Vision

CSE 152, Winter 2019

Instructor: Manmohan Chandraker
Email: mkchandraker [AT] eng [DOT] ucsd [DOT] edu

Lectures: WF 6:30-7:50pm in CENTR 113
Instructor office hours: Th 4-5pm in CSE 4122

TAs: Shashank Shastry (, Bekhzod Soliev (, Yu-Ying Yeh (
TA office hours: M 6-7pm in CSE B275, Th 9-10am in CSE B240A

Discussion section: Tu 8-8:50pm in HSS 1330

Class discussion and message board: Piazza


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.

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 augmented reality. 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 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.

Course Format and Requirements

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 may take the class with a letter grade option for 4 units. Grades will be weighted as 40% for a final exam, 30% for homeworks, 25% for a mid-term and 5% for participation. There will be three homework assignments.


The course will cover a diverse range of topics in computer vision and machine learning. An inexhaustive list includes:



Manmohan Chandraker
Last modified: Fri, Mar 08, 2019