syllabus
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syllabus [2018/09/04 21:12] – jelder | syllabus [2018/11/07 18:01] (current) – jelder | ||
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are not major courses.) | are not major courses.) | ||
- | It also is recommended that students enter this course with a good working knowledge of the calculus of several variables and linear algebra. Familiarity with linear systems theory (e.g., EECS 3451, formerly COSC 4451 and CSE 3451), comfort with elementary manipulation of complex variables and previous experience equivalent to a university level introduction to physics course also would be of value. If in doubt, then consult with the instructor. | + | It also is recommended that students enter this course with a good working knowledge of the calculus of several variables and linear algebra. Familiarity with linear systems theory (e.g., EECS 3451, formerly COSC 4451 and CSE 3451), comfort with elementary manipulation of complex variables and previous experience equivalent to a university level introduction to physics course also would be of value. If in doubt, then consult with the instructor. The primary software environment used for this course will be MATLAB. |
==== Instructor & TAs ==== | ==== Instructor & TAs ==== | ||
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* [[https:// | * [[https:// | ||
* Office: LAS 2052 | * Office: LAS 2052 | ||
- | * Office hours: | + | * Office hours: |
* Email: hakkicankaraimer@gmail.com | * Email: hakkicankaraimer@gmail.com | ||
==== Textbooks ==== | ==== Textbooks ==== | ||
- | The required textbook | + | The required textbook is: Computer Vision Algorithms and Applications, |
+ | * Hardcopy: available at the York Bookstore; one copy is on reserve at the Steacie Library. | ||
+ | * [[http:// | ||
+ | * [[https:// | ||
- | Computer Vision | + | Supplementary (Optional): |
- | by Richard Szeliski, Springer, 2011. | + | * Multiple View Geometry in Computer Vision, Hartley R & Zisserman A, 2004 |
+ | * Pattern Recognition and Machine Learning, Bishop CM, 2006 | ||
+ | * [[http:// | ||
- | Errata for the textbook is available [[https:// | + | ==== Lectures ==== |
- | This text is available at the York University Bookstore | + | * MW 10:00AM - 11: |
+ | * Students are responsible for all material covered in lectures. | ||
+ | * Lecture slides will be posted incrementally | ||
- | ==== Workload | + | ==== Assigned Readings |
- | * Lectures: Students are responsible for all material covered in lectures. Lecture slides will be posted incrementally on this website; see the Schedule link on the side bar to this page. | ||
* Assigned Readings: Students are responsible for all material assigned as reading in the textbook; see the Schedule link on the side bar to this page. | * Assigned Readings: Students are responsible for all material assigned as reading in the textbook; see the Schedule link on the side bar to this page. | ||
- | * Labs: There will be six two-hour labs, these will be held at 4pm-6pm on selected Mondays; see the Schedule link on the side bar to this page. The TA will be present at each lab to provide demonstrations and guidance. | + | |
- | * Assignments: | + | ==== Labs ==== |
- | * Midterm: | + | |
- | * Project: Students will complete a project involving the implementation of a computer vision | + | * Six two-hour labs held in Bergeron 211. |
+ | * 4pm-6pm on selected Mondays | ||
+ | * Laptop-based: | ||
+ | * Desks do not have power - please make sure laptop batteries are fully charged or bring a long power cable to plug into the wall. | ||
+ | * Primary software environment: | ||
+ | * The TA will be present at each lab to provide demonstrations and guidance. | ||
+ | * Students are responsible for all material presented in labs. | ||
+ | * Labs 1, 2, 4 and 5 will be associated with the four short assignments (see below). | ||
+ | * Lab 3 will be used to help students prepare for the midterm. | ||
+ | * The final lab will be used to demo projects and to discuss project reports. | ||
+ | |||
+ | ==== Assignments ==== | ||
+ | |||
+ | * There will be four short assignments that include theory and coding questions | ||
+ | * Due dates can be found on the schedule | ||
+ | |||
+ | ==== Midterm ==== | ||
+ | |||
+ | * In class, closed book. | ||
+ | * Lab 3 will be used to help students prepare. | ||
+ | |||
+ | ==== Project | ||
+ | * Students will complete a project involving the implementation of one or more | ||
+ | * Details can be found on the project page. | ||
==== Course Learning Outcomes ==== | ==== Course Learning Outcomes ==== | ||
- | * Explain the basic terms, concepts and applications of computer vision, including reference to at least one real-world system. | + | * Explain the basic terms, concepts and applications of computer vision. |
* Apply basic mathematical techniques to solve problems in computer vision. | * Apply basic mathematical techniques to solve problems in computer vision. | ||
* Develop software to solve problems in computer vision. | * Develop software to solve problems in computer vision. | ||
- | * Analyze the effects of noise in computer vision algorithms and use appropriate techniques to reduce its effects | ||
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The weight distribution of the course components is as follows: | The weight distribution of the course components is as follows: | ||
- | * 30% - 5 assignments, | + | * 30% - 4 assignments, |
* 30% - Midterm exam | * 30% - Midterm exam | ||
* 40% - Project | * 40% - Project | ||
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* 20% - Final Report | * 20% - Final Report | ||
- | Each piece of work will be assigned a numeric grade. A final numeric grade will be computed using the weighting given above. The final letter grade will be determined from the numeric score using the standard Computer Science and Engineering mapping. |
syllabus.1536095559.txt.gz · Last modified: 2018/09/04 21:12 by jelder