Statistical Pattern Recognition course page

Current semester (Spring 2012):

• Syllabus
• Calendar, Announcements and grades

Lecture Notes:

• Lec0- An Introduction to Matlab (1370 kB)
• Lec1- Course overview (976 kB)
• Lec2- Mathematical review (550 kB)
• Lec3- Feature space and feature selection (343 kB)
• Lec4- Dimensional reduction (feature extraction) (977 kB)
• Lec5- Classification – Introduction (287 kB)
• Lec6- Classification – Probabilistic Methods (552 kB)
• Lec7- Classification – Discriminant Functions (424 kB)
• Lec8- Classification – Modeling (309 kB)
• Lec9- Classification – Neural networks concepts (552 kB)
• Lec10- Classification – Support Vector Machines (639kB)
• Lec11- Classification – Kernel methods (470 kB)
• Lec12- Classification – Graphical Methods (398 kB)
• Lec13- Clustering (374 kB)
• Lec14- Expectation Maximization (EM) and Mixture Models (695 kB)
• Lec15- Semi-supervised classification (305 kB)
• Lec16- Combining methods (158 kB)
• Applications:
  • Handwritten digit recognition (375 kB)
  • Image categorization (1446 kB)
  • Active learning (164 kB)
  • Object tracking (438 kB)
  • Complex networks (284 kB)

Assignments:

• Spring 2012: HW1 (Sup. data), HW2, HW3 (Sup. data), HW4 (Sup. data, libsvm 32bit, libsvm 64bit), HW5, HW6 (Sup. data)
• Spring 2011: HW1 (comp. files), HW2 (comp. files), HW3, HW4, HW5
• Spring 2010: HW1, HW2, HW3, HW4, HW5, HW6

Quizzes:

• Spring 2012: Q1(P,S), Q2(P,S), Q3(P,S), Q4(P,S), Q5(P,S), Q6(P,S), Q7(P,S), ME1(P,S), Q8-1(P,S), Q8-2(P,S), ME2(P,S), Q9(P,S)
• Spring 2011: Q1(P,S), Q2(P,S), Q3(P,S), Q4(P,S), Q5(P,S), Q6(P,S), Q7(P,S), Q8(P,S), ME1(P,S), Q9(P,S), ME2(P,S), Q10(P,S)
• Spring 2010: Q1(P,S), Q2(P,S), Q3(P,S), Q4(P,S), Q5(P,S), Q6(P,S), Q7(P,S), Q8(P,S), Q9(P,S)

Course Project:

• Spring 2012: Project description, Dataset (7.2 MB)
• Spring 2011: Project description, Dataset, Project Contest

Past Exams:

• Spring 2012: Midterm (Solution) – Final (Solution)
• Spring 2011: Midterm – Final
• Spring 2010: Midterm – Final
• Spring 2008: Midterm – Final
• Spring 2007: Midterm – Final
• Fall 2006: Midterm – Final

Useful Links:

• Books and Tutorials
  • Pattern Recognition and Machine Learning book’s website (By Bishop)
  • Pattern Recognition book’s website (By Theodoridis and Koutroumbas)
  • Statistical Data Mining Tutorials (By Andrew Moore)
  • Probabilistic Graphical Methods book’s website (By Koller and Friedman)
  • The Elements of Statistical Learning book’s website (By Hastie, Tibshirani and Friedman)
• Related Courses
  • Michigan State University pattern recognition and analysis course page (Anil K. Jain)
  • Statistical Learning
• Applets
  • Statistics and Distributions, PCA, PCA, Least Square, Perceptron, Parzen Window, KNN 1, KNN2, 2D-Classifiction SVM, LibSVM, Kernel Density Estimation, Baysian Network, K-means, C-Means, Hierarchical Clustering, EM, GMM, AdaBoost
• Matlab Toolboxes
  • Statistical Pattern Recognition Toolbox
  • PRTools: The Matlab Toolbox for Pattern Recognition
  • Bayes Net Toolbox
  • HMM Toolbox
  • LIBSVM – A Library for SVM
• Matlab tutorials
  • Matlab Primer v2
  • Matlab Tutorial by the University of New Hampshire
  • Matlab Tutorial by Indiana University
• Others
  • International Association for Pattern Recognition (IAPR)
  • IAPR Technical Committee 2 on Structural and Syntactical Pattern Recognition
  • IAPR Education Committee Resources (Tutorials, data sets, codes, etc.)
  • IEEE Computer Society Technical Committee on Pattern Analysis and Machine Intelligence (PAMI)
  • Kernel Machines web site
  • Pattern Recognition on the Web

This page will be updated continuously during the spring 2012 semester.