Weekly outline

  • General

    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