Textbooks

The primary materials for the class are lecture slides, notes, and readings listed on the lectures page. There is no required textbook for this class. However, the following textbooks, though geared toward a graduate audience, may be helpful as supplementary resources.

We will post links to sections of Szeliski’s book for each lecture. These readings are not required, but they might be helpful especially if you want to dig deeper into specific topics.

And these books are useful references for for machine learning. We will only briefly touch on ML in this course, but ML is increasingly becoming a key component of modern computer vision systems.

Required Background

The course requires proficiency in Python programming and a background in linear algebra, probability, and statistics. While we will cover the basics of image processing and visualization in Python, you may find the resources below helpful for refreshing your math and programming skills.

Writing is a key component of all projects. Here are some tips on how to write your homework reports. This was designed for the graduate version of the class (COMPSCI 670), but a lot of this applies to 370 reports as well.

Grading

The grading breakdown for this class is as follows:

  • 45% Homework (5 in total)
    • The weights for each homework will be proportional to the points for the homework. For example, a homework worth 80 points will be double the weight of another homework that is worth 40 points.
  • 20% Midterm exam
  • 30% Final exam
  • 5% In-class participation.
    • There will be 15–20 low-stakes quizzes throughout the semester during class. The lowest 25% of these quiz scores will be dropped, and participation credit will be based on 50% participation points and 50% correctness. We will not offer makeup exams for quizzes.

The cutoffs for the letter grades vary slightly each year, but here are what we used last year: A (92), A- (89), B+ (84), B (79), B- (72), C+ (67), C (62), C- (58), D+ (55), D (50).

Course Policies

  • Attendance: Attendance is not mandatory but is strongly encouraged. In addition to earning participation points through in-class quizzes, important announcements will primarily be made during lectures. While lecture recordings are available, they are often unreliable (see Echo360 below). Therefore, it is the responsibility of students to stay up to date with announcements made in class if they miss a lecture.
  • Pass / fail (undergrads): This is requested through the university.
  • Free late days: 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. There is no need to ask for permission for late days – we will automatically figure late days based on the latest gradescope submissions. Delay of even a single minute counts as a full late day, so do not wait till the last minute to submit.
  • Documented late days: Beyond the seven “free” late days, we will only provide “verified” late days as required by university policy listed here. For example, illness documented by a doctors note.
  • Undocumented late days: If you submit your homework late for any reason not covered by the above two policies, you may include at the top of your submission a justification for why it was submitted late. When assigning grades at the end of the semester, the instructor will consider all these justifications and may, at his sole discretion, waive some or all of the penalties applied. The instructor may also waive penalties for submissions that are only very slightly late (e.g. 15 seconds after the deadline). No feedback will be given before the end of the semester about how these decisions will be made. Please make sure to include this in the submission itself and not, e.g., by emailing the instructor.
  • Makeup exam: We will offer a makeup exam (midterm and final) for extenuating personal circumstances as per the university policies.
  • Academic honesty policy: You are required to list the names of anyone you discuss problems with on the first page of your solutions. This includes teaching assistants or instructors. Copying any solution materials from external sources (books, web pages, etc.) or other students is considered cheating. To emphasize: no detectable copying is acceptable, even, e.g., copying a single sentence from an outside source. Sharing your code or solutions with other students is also considered cheating. Any detected cheating will result in a grade of -100% on the assignment for all students involved (negative credit), and potentially a grade of F in the course. In any instance of academic dishonesty, SAT/UNSAT grading will not be approved. Students are expected to be familiar with this policy and the commonly accepted standards of academic integrity (http://www.umass.edu/honesty).
  • Re-grading policy: Despite the best efforts of the course staff, grading errors on assignments and exams can occur. If you believe there is an error in your grade, please submit a re-grade request on Gradescope. Re-grade requests must be submitted within one week of the assignment being returned. Note that re-grading may result in your original grade being adjusted upward or downward, as appropriate.
  • Echo360: Recorded videos will be posted after each lecture. Please note that these recordings are provided on a best-effort basis—they are collected automatically, and the system may occasionally fail. Therefore, do not rely on the recordings as your primary source of information.
  • Use of AI is not permitted: This course assumes that all work submitted by students will be their own, created individually or collaboratively in groups as specified by the assignment instructions. Students must not have any other person or entity complete any portion of an assignment on their behalf. This includes hiring individuals or companies to write assignments and using artificial intelligence tools such as ChatGPT or Copilot.
  • Announcements: We will make announcements during lectures and on Piazza. It is your responsibility to stay informed and adhere to the appropriate deadlines.

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. For further information, please visit Disability Services.

Title IX Statement

In accordance with Title IX of the Education Amendments of 1972 that prohibits gender-based discrimination in educational settings that receive federal funds, the University of Massachusetts Amherst is committed to providing a safe learning environment for all students, free from all forms of discrimination, including sexual assault, sexual harassment, domestic violence, dating violence, stalking, and retaliation. This includes interactions in person or online through digital platforms and social media. Title IX also protects against discrimination on the basis of pregnancy, childbirth, false pregnancy, miscarriage, abortion, or related conditions, including recovery. There are resources here on campus to support you. A summary of the available Title IX resources (confidential and non-confidential) can be found at the following link.

Acknowledgements

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.