Computer Vision (CS-GY 6643)

Grading breakdown: In-class midterm 25%, Programming Projects 75%

Online Discussion: Preferred course communication will be via Slack, so please join our site from using this link: nyucomputervision.slack.com. All questions should also be posted to Slack (not sent via emails). We prefer that lectures or homework questions are asked publicly, since they will often help your classmates. Slack also supports private questions through direct messages for things relevant only to you.

Python and Jupyter: Demos and labs in this class use Python, run through Jupyter notebooks. Jupyter lets you create and edit documents with live Python code and rich comments and images. We suggest that students run their Jupyter notebooks via Google Colaboratory, and we will share them via Colab.

Assignments: Individual programming projects must be turned in to Brightspace by the specified deadline (11:59pm of the due date). Programming projects should be turned in as specified in the project. Note that projects include a competitive component or involve ranking based on performance, we will guide you on the submission for the same. For written project reports, we encourage using LaTeX. You can use this template for LaTeX. While there is a learning curve, these tools typically save students time in the end! If you do write problems by hand, scan and upload as a PDF. Discussion is allowed on homework, but solutions and code must be written independently. See the syllabus for policies. We have a zero tolerance policy for copied code or solutions: any students with duplicate or very similar material will receive a zero on the offending assignment.

Late policy: Every hour that an assignment is late (rounded down) will cause 1% penalization of the total allotted grade. For example, an assignment that is 11 hours 45 minutes late will have a maximum possible score of 89%.

Textbooks: Computer Vision (2nd edition) by Szeliski will accompany the lectures that we cover and specific chapters from this book will be mentioned under reading materials in the schedule below. Textbook is freely available digitally at https://szeliski.org/Book/download.php.

AI Policy: Projects in this class will be divided into two tracks: AI Track and Human Track. Students in the AI Track are allowed to use AI for debugging, writing code, and other use cases. Students in the Human Track are only allowed to use AI for debugging report generation; and not code generation, ideation or any other uses. Students are allowed to use AI if they declare it in the team selection sheet shared along with the project. Student can switch from the Human to the AI track during a project, however, they cannot switch back from the AI track to the Human Track. Students are allowed to pick different tracks for different projects. Students will be curved based on the track they choose.

Tutorials:Linear algebra,Google Colab, Python

The previous iteration of the course is available here.