The primary material for the class are lectures and readings from books, research papers, articles listed on the lectures page. There is no required textbook for this class. Nevertheless the following textbooks are useful:

We will post links to sections of Szeliski’s book for each lecture.

And these for machine learning:

Required Background

The course assumes a strong ability to program in Python and background in linear algebra, probability and statistics. Take a look at the resources below to brush up your math and programming skills.

Writing is a key component of all projects. Here are some tips on how to write well.


The grading breakdown for this class is as follows:

  • 80% Homeworks (5 in total)
  • 20% Project

Note that this class does not have any tests or exams.

The cutoffs for letter grades vary each year, but here are the cutoffs we used in a previous offering of the class: A (92), A- (87), B+ (83), B (79), B- (75), C+ (70), C (65), C- (60), D+ (55), D (50).

Homework Assignments

There will be 5 homework assignments over the course of the semester. These assignments may contain material that has been covered by published papers and webpages. It is a graduate class and we expect students to solve the problems themselves rather than search for answers.

Collaboration Policy

Homework assignments must be done individually: This means that each student must hand in their own answers. However, it is acceptable to collaborate when figuring out answers and to help each other solve the problems. We will be assuming that, as participants in a graduate course, you will be taking the responsibility to make sure you personally understand the solution arising from such collaboration.

You also must indicate on each homework with whom you have collaborated.

Late Policy

  • You can use 7 late days, with up to 3 late days per assignment. Beyond 3 late days the assignment will not be counted at all.
  • Once you have used all 7 late days, penalty is 25% for each additional late day.
  • We will use your latest submission for grading and for calculating your late day usage.
  • There is no bonus if you don’t use late days at all.

Regrade Policy

If you feel that we have made a mistake in grading your homework, please submit a regrading request on Gradescope and we will consider your request. Please note that regrading of a homework may cause your grade to go either up or down.


The project will be carried out in groups of 2 people, and has four main parts: a proposal, a midway report, a final report, and a poster/oral presentation. The project is an integral part of this class, and is designed to be as similar as possible to researching and writing a conference-style paper.

Please see the project page for more information about the final project.


To satisfy the auditing requirement, you must do one of the following:

  1. Submit two homeworks and receive at least 75% of the points on each one.
  2. Submit one homework and do a class project which must address a topic related to computer vision and must be somthing that you have started while taking the class (i.e. it can’t be your previous work). You will need to submit a project proposal with everyone else, present the project with everyone, and write the final report. You must get at least 75% of the points in both the homework and the project.

Past offerings of 670 at the university

Related courses at the university

  • 682: Neural Networks: A Modern Introduction
  • 590: Intelligent Visual Computing
  • 373: Introduction to Computer Graphics
  • 370: Introduction to Computer Vision

Accommodation Statement

The University is committed to providing an equal educational opportunity for all students. If you have a documented physical, psychological, or learning disability on file with Disability Services (DS), you may be eligible for reasonable academic accommodations to help you succeed in this course. If you have a documented disability that requires an accommodation, please notify me within the first two weeks of the semester so that we may make appropriate arrangements.

Academic Honesty

Since the integrity of the academic enterprise of any institution of higher education requires honesty in scholarship and research, academic honesty is required of all students. Students are expected to be familiar with this policy and the commonly accepted standards of academic integrity (http://www.umass.edu/honesty).


Many of the slides and homework assignments are based on excellent computer vision courses taught elsewhere by Svetlana Lazebnik, Alyosha Efros, Alexander Berg, Steven Seitz, James Hays, Charless Fowlkes, Kirsten Grauman and many others. Many thanks to Richard Szeliski for making the computer vision textbook available online for free.