https://wiki.eecs.yorku.ca/course_archive/2016-17/F/6327/ 2026-05-11T11:12:08+00:00 https://wiki.eecs.yorku.ca/course_archive/2016-17/F/6327/ https://wiki.eecs.yorku.ca/course_archive/2016-17/F/6327/_media/wiki:dokuwiki-128.png text/html 2018-01-12T16:28:45+00:00 Anonymous (anonymous@undisclosed.example.com) https://wiki.eecs.yorku.ca/course_archive/2016-17/F/6327/assignment?rev=1515774525&do=diff * Assignment One * Assignment Two text/html 2012-08-30T18:58:29+00:00 Anonymous (anonymous@undisclosed.example.com) https://wiki.eecs.yorku.ca/course_archive/2016-17/F/6327/contact?rev=1346353109&do=diff Contact Course Director * Hui Jiang * Office: CSE 3014 (Computer Science and Engineering Building) * Phone: (416) 736-2100 x. 33346 * Email: hj AT cse DOT yorku DOT ca You may contact the course director by e-mail at any time. Office Hours You may visit the Course Director during the following regularly scheduled office hours: text/html 2016-09-12T21:11:02+00:00 Anonymous (anonymous@undisclosed.example.com) https://wiki.eecs.yorku.ca/course_archive/2016-17/F/6327/course_outline?rev=1473714662&do=diff Course Outline The course outline is a guideline to topics that will be discussed in the course, and when they will be discussed: Weeks 1 and 2 Machine Learning (basic concepts) Math Background: probabilities; Bayes theorem; statistics; estimation; regression; hypothesis testing; entropy; mutual information; decision tree; optimization theory and convex optimization; matrix decomposition text/html 2007-08-01T14:34:50+00:00 Anonymous (anonymous@undisclosed.example.com) https://wiki.eecs.yorku.ca/course_archive/2016-17/F/6327/faq?rev=1185978890&do=diff Frequently Asked Questions Here, you can list frequently asked questions from your students along with responses. General Do I need to register for a CSE Computer Account? Yes. Do I need to attend class? If you wish to pass. Tests Do I need to write all tests? text/html 2007-08-02T17:22:57+00:00 Anonymous (anonymous@undisclosed.example.com) https://wiki.eecs.yorku.ca/course_archive/2016-17/F/6327/forums?rev=1186075377&do=diff Forums This page acts as a future placeholder for a link to a forum system. text/html 2016-08-29T19:27:02+00:00 Anonymous (anonymous@undisclosed.example.com) https://wiki.eecs.yorku.ca/course_archive/2016-17/F/6327/grades?rev=1472498822&do=diff Grades The weight distribution of the course components is as follows: * 10% - Class participation * 30% - Two Assignments: basic concepts, principles, algorithms * 40% - Two lab projects: project one (20%) and project two (20%). * 20% - Self-study an advanced topic and presentation text/html 2016-11-25T22:08:31+00:00 Anonymous (anonymous@undisclosed.example.com) https://wiki.eecs.yorku.ca/course_archive/2016-17/F/6327/important_dates?rev=1480111711&do=diff Important Dates Here, Important dates for this course: * Dec 30: Project2 is due. * Nov 25 and 30: In-class presentations. * Nov 18 Dec 2: A2 is due in class. * Nov 10 Nov 18: Project1 is due. * Oct 21: A1 is due in class. text/html 2019-08-22T21:20:09+00:00 Anonymous (anonymous@undisclosed.example.com) https://wiki.eecs.yorku.ca/course_archive/2016-17/F/6327/lecture_notes?rev=1566508809&do=diff * Weeks 1-2: A. Machine Learning (basic concepts); B. Math foundation (updated a bit): probabilities, Bayes theorem, statistics, Entropy, mutual information, decision tree, optimization, matrix decomposition (Weekly Reading: W2) (A useful online manual on Matrix Calculus) text/html 2007-07-31T18:55:53+00:00 Anonymous (anonymous@undisclosed.example.com) https://wiki.eecs.yorku.ca/course_archive/2016-17/F/6327/policies?rev=1185908153&do=diff Policies Here, you can list specific course policies. Academic Dishonesty For more information on Academic Dishonesty, click here. text/html 2016-11-27T21:39:50+00:00 Anonymous (anonymous@undisclosed.example.com) https://wiki.eecs.yorku.ca/course_archive/2016-17/F/6327/presentations?rev=1480282790&do=diff Advanced Topics for self-study and Presentation Advanced Topics for self-study and Presentation The presentations will be organized into two afternoon sessions: 1-5pm on Nov 25 (Friday) @HNE 037 and 1-5pm on Nov 30 (Wednesday) @LAS3033 . Each person will make a 20-25 min presentation (including Q&A). To save time, you need to email your PPT to a session chair (to be named) before 10am that day (otherwise, your mark will be deducted). text/html 2018-01-12T16:28:58+00:00 Anonymous (anonymous@undisclosed.example.com) https://wiki.eecs.yorku.ca/course_archive/2016-17/F/6327/projects?rev=1515774538&do=diff * Project one (extended to Nov 18) * Project Two: Your project 2 report will be submitted as a CS conference paper. You may use any latex temple from CVPR, ACL, ICML, etc. It is expected to be 6-8 pages (double-columned). I will mark your report as a conference reviewer based on format, writing style, work quality, extra additions to the existing methods, etc.. text/html 2007-07-31T18:59:29+00:00 Anonymous (anonymous@undisclosed.example.com) https://wiki.eecs.yorku.ca/course_archive/2016-17/F/6327/resources?rev=1185908369&do=diff Resources Use this page to list additional Resources that may be helpful to your students. * Department of CSE Home Page * York Exam Schedule text/html 2024-03-08T13:45:38+00:00 Anonymous (anonymous@undisclosed.example.com) https://wiki.eecs.yorku.ca/course_archive/2016-17/F/6327/sidebar?rev=1709905538&do=diff * What's New * Course Outline * Lecture Notes * Important Dates * Assignment * Presentation * Projects * Grades * Textbook * Contact text/html 2016-09-14T20:23:46+00:00 Anonymous (anonymous@undisclosed.example.com) https://wiki.eecs.yorku.ca/course_archive/2016-17/F/6327/start?rev=1473884626&do=diff EECS 6327 Probabilistic Models & Machine Learning (Fall 2016) Description The course introduces some probabilistic models and machine learning methods. The covered topics may include: * Bayesian Decision theory, Generative vs Discriminative modelling text/html 2016-08-29T19:28:31+00:00 Anonymous (anonymous@undisclosed.example.com) https://wiki.eecs.yorku.ca/course_archive/2016-17/F/6327/textbook?rev=1472498911&do=diff Textbook There is no required textbook for this course. Three optional reference books: * C. M. Bishop, Pattern Recognition and Machine Learning, Springer, ISBN 0-387-31073-8. * R. O. Duda, P. Hart and D. Stork, Pattern Classification (2nd Edition), John Wiley & Sons, Inc., ISBN 0-471-05669-3. text/html 2016-11-25T22:10:05+00:00 Anonymous (anonymous@undisclosed.example.com) https://wiki.eecs.yorku.ca/course_archive/2016-17/F/6327/whats_new?rev=1480111805&do=diff What's New? Refresh your page!!! * Nov 25: Assignment two (A2) is extended to Dec 2 (Fri). Please email me a PDF copy of A2 before the deadline. * Nov 18: The details for presentation have been finalized, check Presentation. * Nov 15: The classrooms for advanced topic presentations have been finalized. We are in