EE462, EE9SO25, EE9CS728:
Machine Learning for Computer Vision 

 

    Course Formats

Autumn term 2013: every Monday, at 9-11am (2hours), in the room 509A, EEE, from 7th Oct to 9th Dec

Assessment: 3 hour (closed book) exam (100%) in the start of Summer term 2014

Prerequisite: None

Lecturer: Dr Tae-Kyun Kim (office: 1017, e-mail: tk.kim@imperial.ac.uk)

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

Textbook (optional): Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer


    Course Aims

The course aims to introduce the concepts, theories and state-of-the-art algorithms for visual learning and recognition. The first half of the module, for formulations and theories of machine learning techniques, consists of clustering, discriminative classifier learning, and probabilistic generative models. The latter half leads to the topics of visual recognition by the machine learning techniques learnt, including object detection/categorisation, segmentation, face and pose recognition. Each topic is illustrated with case studies.

Syllabus:

Machine learning techniques: Prob. theory, Polynimial curve fitting, Clustering and EM, Boosting, Randomised decision forests, Maximum margin classifier, Markov random fields, Manifold learning and Gaussian process.

Visual learning and recognition: Image clustering, Object detection, Bag of words, Object recognition/categorisation, Segmentation, Face recognition and Pose estimation.


    Course Schedule and Lecture Notes

Lecture 1-2: Course Overview / Background

   [Lecture note in ppt]
   [Supplementary document]


Lecture 3-4: Clustering and EM

   [Lecture note in ppt]
   [Supplementary document]
   [Statistical Pattern Recognition Toolbox for Matlab]


Lecture 5-6: Object Detection / Boosting

   [Lecture note in ppt]
   [Supplementary document]


Lecture 7-8: Random Forest and Example paper

   [Lecture note in ppt]
   [Example paper]


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

   [Lecture note in ppt]
   [Supplementary document]
   [Statistical Pattern Recognition Toolbox for Matlab]


Lecture 11-12: Segmentation / Markov Random Fields

   [Lecture note in ppt]
   [Supplementary document]
   [Matlab demo code for image denoising by MRFs]


Lecture 13-14: Face Recognition / Manifold Learning

   [Lecture note in ppt]
   [Supplementary document]
   [Matlab demo code for face recognition by PCA]


Lecture 15-16: Pose Estimation / Gaussian Process

   [Lecture note in ppt]
   [Supplementary document]
   [Matlab toolbox for GP]


Lecture 17-18: Summary and Example paper

   [Exam paper 2011/2012, 2012/2013]


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