The primary material for the class are lectures and readings listed on the lectures page. There is no required textbook for this class. Nevertheless, the following textbooks might be useful, even though they are aimed at a graduate audience.
- Computer Vision: A Modern Approach by David Forsyth and Jean Ponce (2nd ed.)
- Computer Vision: Algorithms and Applications, by Richard Szeliski (2nd ed.) (available online).
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.
- Machine Learning: A Probabilistic Perspective by Kevin P. Murphy
- The Elements of Statistical Learning, T. Hastie, R. Tibshirani, and J. Friedman
- Pattern Recognition and Machine Learning, Christopher M. Bishop (available online)
- Machine Learning, Tom M. Mitchell
- A Course in Machine Learning, Hal Daumé III (available online)
- Deep Learning, Goodfellow, Bengio, and Courville (available online)
The course requires an ability to program in Python and background in linear algebra, probability and statistics. We will cover basics of image processing and visualization in python, but you might these the resources below helpful to brush up your math and programming skills.
- NumPy tutorial from Stanford CS231n
- Vector geometry notes by Denis Sevee
- Linear algebra review (via David Kriegman)
- Random variables review (via David Kriegman)
- Deep learning: Pytorch, TensorFlow
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:
- 55% Homeworks (4 or 5 in total)
- 15% Midterm exam
- 25% Final exam
- 5% Class participation
Class participation will be based on
- 4% attendance in class (you should attend at least 75% of the classes)
- 1% participation in piazza (response to polls, quizzes, questions, etc)
The cutoffs for letter grades are: A (95), A- (90), B+ (85), B (80), B- (75), C+ (70), C (65), C- (60), D+ (55), D (50).
- 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, with documentation (i.e. 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.
- 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: Errors in grading of assignments and exams can occur despite the best efforts of the course staff. If you believe you’ve found a grading error, submit a re-grade request on gradescope. Re-grade requests must be submitted no later than one week after the assignment is returned. Note that re-grading may result in your original grade increasing or decreasing as appropriate.
- Echo360: Recorded videos will be posted two weeks after the lecture. Under special circumstances the instructor might provide early access (e.g., documented absence). Note that recordings are provided as a best effort from our side – they are collected automatically and the system fail sometimes. So do not rely on the recordings as your primary option.
- Announcements: We will make announcements during lectures and on piazza. It is your responsibility to follow these, and meet the appropriate deadlines.
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.
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.