CSCI 3343: Computer Vision

• Prerequisites: Maths (Linear Algebra, Multivariate Calculus) and Coding (Python)
• Grading: No quizzes/exams. 10 psets (70%) + 1 final project (30%) + extra credits for completing labs (0.5 pt each)
• Video resource: computer vision (prof Nayar @Columbia U.), deep learning (prof. Ng @Stanford U.), deep learning for computer vision (prof. Johnson @UMich)

• Lectures (MW): Do the reading before the lecture.
• Labs (F): Bring your laptop.
• Textbook: We'll be using the following two online books:
  • [Chap. 1-3, 5-9] Szeliski. Computer Vision: Algorithms and Applications, 2nd edition draft (available for free online)
  • [Chap. 1-9, 11] Goodfellow, Bengio, Courville. Deep Learning. (available for free online)

• Dropbox: The lecture pdfs will be uploaded to Dropbox (follow the link) and you can ask questions there by making comments on the slides directly.
• Discord: For labs/psets/final projects, we will create dedicated channels for you to ask public questions. If you cannot make your post public (e.g., due to revealing problem set solutions), please create a room with instructors and TAs separately, or come to office hours. Please note, however, that the course staff cannot provide help debugging code, and there is no guarantee that they'll be able to answer last-minute homework questions before the deadline. We also appreciate it when you respond to questions from other students! If you have an important question that you would prefer to discuss over email, you may email the course staff, or you can contact the instructor by email directly.
• Support: The university counseling services center provides a variety of programs and activities.
• Accommodations for students with disabilities: If you are a student with a documented disability seeking reasonable accommodations in this course, please contact Kathy Duggan, (617) 552-8093, dugganka@bc.edu, at the Connors Family Learning Center regarding learning disabilities and ADHD, or Rory Stein, (617) 552-3470, steinr@bc.edu, in the Disability Services Office regarding all other types of disabilities, including temporary disabilities. Advance notice and appropriate documentation are required for accommodations.

Grades will be computed as follows, with all homeworks equally weighted:

Pset	70 points
Final project	30 points
Lab	0.5 point extra credit each

• Pset: There will be a problem set approximately every week. All programming assignments are to be completed in Python on Colab, using the starter code that we provide. Assignments will always be due at midnight (11:59 pm) on the due date. The assignments will all be worth approximately equal amounts. You may be asked to annotate your pdf (e.g. by selecting your solution to each problem).
• Final project: (guideline) In lieu of a final exam, we'll have final project. This project will be completed in small groups during the last weeks of the class. The direction for this project is open-ended: you can either choose from a list of project ideas that we distribute, or you can propose a topic of your own. A short project proposal will be due approximately halfway through the course. During the final exam period, you'll turn in a final report and give a short presentation. You may use an ongoing research work for your final project, as long it meets the requirements.
• Lab: The materials are expected to be finished in class. If you submit the correctly finished Lab file before the next Fri class (10 am), you will receive 0.5 point per lab as the extra credit to your final grade. No late policy for the lab. You will receive 0.5 point per lab as the extra credit to your final grade, if your results are correct. Deadline is Dec 13th 11:59 pm.
• Late policy: You'll have 10 late days to use over the course of the semester. Each time you use one, you may submit a homework assignment one day late without penalty (weekends don't count as late days). You do not need to notify us when you use a late day; we'll deduct it automatically. If you run out of late days and still submit late, your assignment will be penalized at a rate of 10% per day until 30% left (before Dec 13th 11:59 pm). If you edit your assignment after the deadline, this will count as a late submission, and we'll use the revision time as the date of submission (after a short grace period of a few minutes). We will not provide additional late time, except under exceptional circumstances, and for these we'll require documentation (e.g., a doctor's note). Please note that the late days are provided to help you deal with minor setbacks, such as routine illness or injury, paper deadlines, interviews, and computer problems; these do not generally qualify for an additional extension.
• Academic integrity: While you are encouraged to discuss homework assignments with other students, your programming work must be completed individually. You may not search for solutions online, or to use existing implementations of the algorithms in the assignments. Thus it is acceptable to learn from another student the general idea for writing program code to perform a particular task, or the technique for solving a mathematical problem, but unacceptable for two students to prepare their assignments together and submit what are essentially two copies of identical work. If you have any uncertainty about the application of this policy, please check with me. Failure to comply with these guidelines will be considered a violation of the University policies on academic integrity. Please make sure that you are familiar with these policies.

Check out the full list! Welcome to drop new questions there.

Q. Err, I learned those prerequisites before, but the memory is blurry now. Do you have any refresher materials?
A. Linear algebra (Essence, Chap 1-4), Calculus (Essence, Chap 1, 3-4, 8-9), Python ("Basics of Python" section).
Q. Hmm, the refreshers don't look too hard, but I'm still not sure if I can handle the course?
A. Pset 1 and Lab 1 will be out on Mon (check out Colab tutorial) and your maths/coding should be good if you can tackle them. It's also good if you can struggle through them to show your learning ability, which in the end is the goal of education.
Q. Wow, the course seems really intense!
A. Computer vision (AI) is super cool, but there is no free lunch. The course materials focus on core concepts and learning-through-coding, so that you can later be quick-to-understand and ready-to-code about AI in your career.
Q. What, final project from day one?!?
A. Relax, nothing serious is due until Oct. 18. We hope to get the best out of you by kindling your passion to apply computer vision to areas you care about dearly. As an intro course, we will only cover the basics for the exciting real-world applications (week 7-12). The final project is an effective way to dive deep into one topic and gain the full-cycle research experience.

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