Syllabus

From CIS 680: Learning and Vision
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Audience

This course is suited for graduate Engineering students with solid knowledge of computer vision and machine learning.

Pre-requisites

Linear Algebra, Computer Vision, Machine Learning, Basic programming knowledge in Python.

Recommended Textbooks

The course does not have a required textbook, however additional references are recommended to supplement the lecture notes:

Related Courses & Tutorials

Grading Policy

Homework Projects: 100%


Late Policy

Students will have total of 5 late days to use during the semester. These late days are used to submit homeworks/projects after the due date with no penalty.

Software

  • We will be using Python for the course.

Collaboration

Honor code

The CIS department encourages collaboration among graduate students. However, it is important to recognize the distinction between collaboration and cheating, which is prohibited and carries serious consequences. Cheating may be defined as using or attempting to use unauthorized assistance, material, or study aids in academic work or examinations. Some examples of cheating are: collaborating on a take-home exam or homework unless explicitly allowed; copying homework; handing in someone else's work as your own; and plagiarism.