Computer Vision

CSE 152A, Fall 2022

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

Lectures: WF 5-6:20pm, at CENTR 115
Instructor office hours: Th Noon-1pm at EBU-3B 4122

TAs: Meng Song (, Mallikarjun Swamy (, Rishikanth Chandrasekaran (, Vishal Vinod (
TA office hours: TBA

Tutors: Nicholas Chua (, Navya Sharma (, Ang Li (
Tutor hours: TBA

Discussion section: M 3-3:50pm in CENTR 119

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 a 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 modeling the geometric and physical process of image formation, or drawing upon prior knowledge based on observations. Recent progress in deep learning has allowed computer vision applications to succeed even in domains considered extremely 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, calculus and probability 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 fundamental concepts in 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 30% for a final exam, 20% for a mid-term, 45% for homeworks and 5% for a self-study exercise. There will be four graded homework assignments and several ungraded quizzes.


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



Manmohan Chandraker
Last modified: Sun, Sep 18, 2022