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========= Assignment 3: Support Vector Machines ============ | ========= Assignment 3: Support Vector Machines ============ | ||
- | Due: October 20th at 6pm | + | Due: October 16th at 11pm |
===== Part 1: SVM with no bias term ===== | ===== Part 1: SVM with no bias term ===== | ||
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===== Submission ===== | ===== Submission ===== | ||
- | Submit your report via Canvas. Python code can be displayed in your report if it is succinct (not more than a page or two at the most) or submitted separately. The latex sample document shows how to display Python code in a latex document. Code needs to be there so we can make sure that you implemented the algorithms and data analysis methodology correctly. Canvas allows you to submit multiple files for an assignment, so DO NOT submit an archive file (tar, zip, etc). | + | Submit the pdf of your report via Canvas. Python code can be displayed in your report if it is succinct (not more than a page or two at the most) or submitted separately. The latex sample document shows how to display Python code in a latex document. Code needs to be there so we can make sure that you implemented the algorithms and data analysis methodology correctly. Canvas allows you to submit multiple files for an assignment, so DO NOT submit an archive file (tar, zip, etc). Canvas will only allow you to submit pdfs (.pdf extension) or python code (.py extension) |
===== Grading ===== | ===== Grading ===== | ||
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Grading sheet for assignment 2 | Grading sheet for assignment 2 | ||
- | Part 1: 45 points. | + | Part 1: 40 points. |
(10 points): Primal SVM formulation is correct | (10 points): Primal SVM formulation is correct | ||
(10 points): Lagrangian found correctly | (10 points): Lagrangian found correctly | ||
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( 5 points): Discussion of the implication of the form of the dual for SMO-like algorithms | ( 5 points): Discussion of the implication of the form of the dual for SMO-like algorithms | ||
- | Part 2: 15 points. | + | Part 2: 10 points. |
Part 3: 40 points. | Part 3: 40 points. |