Course Schedule and Lecture Notes
(lectures notes to be updated)

• Lecture 1-2: Course Overview / Background

   [Lecture note in ppt and pdf]


• Lecture 3-4: Clustering and EM

   [Lecture note in ppt and pdf]


• Lecture 5-6: Object Detection / Boosting

   [Lecture note in ppt and pdf]


• Lecture 7-8: Random Forest and Applications

   [Lecture note in ppt and pdf]


• Lecture 9-10: Object Recognition, Categorisation / Maximum Margin Classifier

   [Lecture note in ppt and pdf]


• Lecture 11-12: Segmentation / Markov Random Fields

   [Lecture note in ppt and pdf]


• Lecture 13-14: Face Recognition / Manifold Learning

   [Lecture note in ppt and pdf]


• Lecture 15-16: Pose Estimation / Gaussian Process (cancelled)

   [Lecture note in ppt]


• Lecture 17: Summary


Resources:
   [Statistical Pattern Recognition Toolbox for Matlab]
   [Matlab demo code for image denoising by MRFs]
   [Matlab toolbox for GP]

Coursework Schedule and Material

See the coursework guidelines. The coursework material are released on time, not earlier, at this website and Blackboard.

All course works require Matlab programming. Some questions marked* are about theories, answering them does not require Matlab programming.

Courseworks are due about every two weeks. Refer to the schedules and mark percentages below to help your planning. They are tentative, and subject to minor changes depending on our progress. The final score will not be the sum of all marks above. They will be moderated, as appropriate.


• Coursework 1: 15%, release: 13 Oct 2014, due: 26 Oct 2014 (11:59pm)

    Contents: Matlab basics, Matlab image interfaces, Lecture 1-2
    [Coursework material]


• Coursework 2: 25%, release: 27 Oct 2014, due: 9 Nov 2014 (11:59pm)

    Contents: Lecture 3-4,5-6
    [Coursework instruction and data/code]


• Coursework 3: 25%, release: 17 Nov 2014, due: 30 Nov 2014 (11:59pm)

    Contents: Lecture 7, 9-10
    [Coursework instruction and data/code]


• Coursework 4: 25%, release: 01 Dec 2014, due: 14 Dec 2014 (11:59pm)

    Contents: Lecture 11-12, 13-14
    [Coursework instruction and data]


• Individual interview: 10%, 15 Dec 2014 (or 08 Dec) (at 9-11am)

    Contents: All courseworks and lectures


The courseworks (reports and Matlab codes) should be submitted to Blackboard electronically. And drop a hardcopy of the report in the homework dropbox at EEE 1017.
Write your full name and CID number on the top of the first page.

Penalty on late submissions is applied, by 10% mark deduction for each day.

*Warning on plagiarism! You will receive 0 mark when you are found to cheat, to copy others’ reports and codes, rather than using provided codes or Matlab default toolboxes. If you are uncertain, please contact the GTAs.

*Please register the course, announcements will be made by the course email-list.

http://www.iis.ee.ic.ac.uk/~tkkim/mlcv.htm