Weekly outline

  • General

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    Textbook & References

    • Pattern Recognition, by S. Theodoridis and K. Koutroumbas , second edition available on moodle, 
    • MATLAB-based examples accompanying the book: An Introduction to Pattern Recognition, by S. Theodoridis and K. Koutroumbas 
    • Richard O. Duda, Peter E. Hart, David G. Stock: Pattern Classification, 2nd edition, John Wiley & Sons, New York, 2001, EUR 114.45 
    • Trevor Hastie, Robert Tobshirani, Jerome Friedman: The Elements of Statistical Learning – Data Mining, Inference, and Prediction, 2nd edition, Springer, New York, 2009, EUR 71.05 http://www-stat.stanford.edu/~tibs/ElemStatLearn/ 
    • Christopher M. Bishop: Pattern Recognition and Machine Learning, Springer, New York, 2006, EUR 75.05 
    • Used Material from Pattern Recognition Course offered by  riedrich-Alexander University of Erlangen-Nuremberg. in 2012/2013 by Joachim Hornegger and Hahn Steidl 
    • More will be added as we need them.

    Grading Criteria

    • Week 7:
      • Assignments (Part of the Project) 10% 
      • Midterm 20%
    • Week 12:
      • Assignments  (Part of the Project) 10%
      • Project presented in Week 12: 20%
    • Final 40%
      • Seminar presented in Week 15: 20%
      • Final Exam in Week 16: 20%

  • 6 October - 12 October

    6th of October Holiday - no class

  • 13 October - 19 October

    Week 1: Introduction

    ILOs:

    • Introduction to pattern recognition applications, approaches
    • Appreciate data visualisation techniques as methods for data mining and decision boundary shape estimation
    • Classifiers' Performance Measures

  • 20 October - 26 October

    Week 2: Probability & Bayes Decision Rule

    ILOs:

    • Revision to Probability Preliminaries
    • Introduction to Bayes Classification
    • Matlab functions to generate random Gaussian distributions, test the bayes decision rule

  • 27 October - 2 November

    Week 3: Bayes Classification

    ILOs:

    • Practice Bayes Decision Rule
    • Solve posterior probability using logistic regression
    • Matlab Exercises 

  • 3 November - 9 November

    Week 4: Naive Bayes & MLE

    ILOs:

    • Understand the theory of Naive Bayes and when its useful
    • Understand how parameter estimation works, and the derivation of Maximum Likelihood Estimation equations.

  • 10 November - 16 November

    Week 5: Gaussian Mixture Models and Expectation-Maximisation Algorithm

    ILOs:

    1. Understand the GMM datasets
    2. Understand how the EM algorithm works for semi-parametric datasets to estimate the density and the parameters iteratively

  • 17 November - 23 November

    Week 6: Non-Parametric Methods

    ILOs:

    1. Understand that datasets don't fit known distributions all the time and can not be described by a set of parameters
    2. Use Density Estimation Methods methods to describe the datasets
    3. Differentiate Parzan Windows (Kernel) method and K-Nearest Neighbours method

  • 24 November - 30 November

    Week 7: Linear Classification Methods

    ILOs:

    1. Understand how linear classification works and their accuracy measures
    2. Understand Linear (Fisher) Discriminant Analysis approach
    3. Understand Perceptron Algorithm and its variations

  • 1 December - 7 December

    Week 8: Linear Classification: SVM

    ILOs:

    • Understand higher dimensions mapping, and Kernel trick using dot product
    • Understand Support Vectors Machines and how they work

  • 8 December - 14 December

    Week 9: Non-Linear Methods 1: Decision Trees

    ILOs:

    • Understand how to build decision trees for non-metric datasets
    • Understand how to calculate the Information and Uncertainty in Every Feature, to  build the most optimal tree
    • Research methods to pruning and approximating tree building algorithms 

  • 15 December - 21 December

    Week 10: Midterm Exam

    The Exam document, dataset, and submission link will be available here Tuesday the 15th of December, 2015 @ 6 p.m.

    Please sign up and enrol in the course, and make sure you can submit. The deadline is Sunday the 20th of December, 2015 @ 8 a.m. in the morning. Please submit different answers and different approaches to qualify for a grade. Good Luck.

    To know your ID, look it up in the attached document. Contact me on manal.helal@gmail.com to confirm your username and enrol you in the course.

  • 22 December - 28 December

    Week 11: Non-Linear Classification: Neural Networks

    ILOs:

    • Understand how multilayer perceptrons can solve non-linear classification problems
    • Understand the different models of Building a Neural Networks Classifier

  • 29 December - 4 January

    Week 12: Non-Linear Classification: Non-Linear SVM

    ILOs:

    • Familiarisation with the different kernel functions, particularly RBF.
    • Understand the Non-linear SVM Formulation.

  • 5 January - 11 January

  • 12 January - 18 January