Of your condition within a week of the assignment deadline.The online learning is a popular trending and great revolution in today’s education. If you have a medical condition which will prevent you from doing aĬertain assignment, you must inform the instructor Will verify your disability and determine reasonable accommodations for If you have a disability for which you are or may be requesting anĪccommodation, you are encouraged to contact both your instructor andĭisability Resources and Services (DRS), 140 William Pitt Union, (412)Ħ48-7890, (412) 228-5347 for P3 ASL users, as early as possible in the term. Please consult the University Guidelines on Academic Integrity. The class and receive disciplinary penalty. When in doubt about what youĬan or cannot use, ask the instructor! Plagiarism will cause you to fail You are also not allowed to search forĬode on the internet, use solutions posted online unless you areĮxplicitly allowed to look at those, or to use Matlab's implementation Written for the assignments, or at their written answers. You are allowed to discuss theĪssignments with your classmates, but do not look at code they might You will do your work (exams and homework) individually. A late day is anything from 1Ĭollaboration Policy and Academic Honesty Up your free late days, you will incur a penalty of 25% from the totalĪssignment credit possible for each late day. The 72-hour "budget" is total for all programming assignments, NOT per assignment. Homework 12 hours late, and another 60 hours late. On your programming assignments only, you get 3 "free" late days counted in minutes, i.e., you can The grading rubric will be as follows: 1 = you attended infrequently, 2 = you attended frequently but did not speak in class, 3 = you attended frequently and spoke a few times, 5 = you attended and participated frequently, 4 = in between 3 and 5. Others' questions on Piazza, or bringing in relevant articles you saw in the news. Making meaningful remarks and comments about the lecture, answering Responding to the instructor's or others' questions, asking questions or YouĬan actively participate by, for example, Attendance will not be taken,īut keep in mind that if you don't attend, you cannot participate. Students are expected to regularly attend the class lectures, and shouldĪctively engage in in-class discussions. There will be no make-up exams unless you or a close relative is seriously ill! Participation The second exam is not cumulative and will not cover material from the first exam. Homework is due at 11:59pm on the due date. Name the file YourFirstName_YourLastName. Your code should be a single zip file with. Your last attempt as a PDF, and look for comments if points were Comments will be provided in CourseWeb (load Your written assignments should be a single Page for CS1674, then click on "Assignments" (on the left) and theĬorresponding homework ID. You will submit your homework using CourseWeb. Programming homework (10 assignments x 4.5% each = 45%).Written homework (10 assignments x 1% each = 10%).Grading will be based on the following components: Pattern Recognition and Machine Learning by Christopher Bishop.Computer Vision: Models, Learning, and Inference by Simon Prince (available for free on author's page).Computer Vision: A Modern Approach by David Forsyth and Jean Ponce.Visual Object Recognition by Kristen Grauman and Bastian Leibe (accessible for free from campus).Programming languages: We will use Matlab, which is available for download for free using Software Downloads in My Pitt.Īnd Applications by Richard Szeliski (available for free on The time when you should ask the instructor or TA questions is during office hours. The instructor and TA will monitor it infrequently. Note that we will use Piazza primarily for classmate-to-classmate discussion of We will cover recently popular techniques such as convolutional and recurrent neural networks.įormat will include lectures, written homework assignments, programming The second part will focus on visual recognition.Īpproaches to object recognition and detection, examine the interplays between vision and language, and learn to model human pose and activity. Will cover fundamental concepts such as image formation, imageįiltering, edge detection, texture description,įeature extraction and matching, and grouping and fitting.
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