CSC334 - Lecture Notes

• Lecture #1 - Matrix Algebra Review
• Lecture #2 - Multiple Regression
• Lecture #3 - Multiple Regression & IML
  Optional - Readings & Applet:
  • Brian Ripley's papers (U Oxford). See the paper "Can statistical theory help us use neural networks better?."
  • NN's and Statistical Models by W S Sarle (SAS Institute)
  • Radial Basis Funcion NN's by M Orr (U Edinburgh)
  • Conjugate Gradient Method by J Shewchuk (UC Berkeley)
  • Predicting Fault Detection Effectiveness (IEEE Proceedings of 4^th International Software Metrics Symposium - 1997) by J Morgan (DePaul U)
• Lecture #4 - Principal Components Analysis (PCA)
• Midterm
• Lecture #6 - Spectral Decomposition & PCA
  Optional - Readings:
  • NFL-2000 PCA, by M. Watnik, & R.A.Levine, Journal of Statistics Education (2001) vol.9,3
  • Multispectral Video, by P. Koponen et al., Proceedings of the International Conference on Pattern Recognition - ICPR'00 (2000)
  • Human activity detection in MPEG sequences, by B. Ozer et al., Workshop on Human Motion - HUMO'00 (2000)
  • Principal Components of Expressive Speech Animation, by Sumedha Kshirsagaret al., Proceedings of Computer Graphics Interational - CGI'01 (2001)
  • AudiCom: A Video Analysis System for Auditing Commercial Broadcasts, by Juan Sanchez et al., IEEE International Conference on Multimedia Computing and Systems 2-II (1999)
• Lecture #7 - Cluster Analysis
  Optional - Readings:
  • A Classification of Pure Malt Scotch Whiskies, by Francois-Joseph Lapointe & Pierre Legendre, Applied Statistics, vol.43, #1 (1994)
  • Interactively Exploring Hierarchical Clustering Results, by Jinwook Seo et al., IEEE Computer (2002)
  • An Efficient k-Means Clustering Algorithm: Analysis & Implementation, by Tapas Kanungo et al., IEEE Transactions on Pattern Analysis & Machine Intelligence (2002)
  • Hierarchical Grouping to Optimize an Objective Function, by J.H.Ward, Jr., Journal of the American Statistical Association, vol58 (1963)
  • Some Methods for Classification and Analysis of Multivariate Observations, by J.B.MacQueen, Proc. 5th Berkeley Symp. Math. Statist. Prob. 1. Univ. of Cal. Press, Los Angeles, Berkeley.
• Lecture #8 - Discriminant Analysis (Fisher's Linear Discriminant Function)
  Optional - Readings:
  • Evaluation of the Detection Rate of Hemophilia Carriers, by B.N.Bouma. et al., Statistical Methods for Clinical Decision Making, vol7#2 (1975)
  • Pitfalls in the Application of Discriminant Analysis in Business, Finance and Economics, by R.A.Eisenbeis, Journal of Finance, vol32 (1977)
  • The Performance of Fisher's Linear Discriminant Function under Non-Optimal Conditions, by W.J.Krzanowski, Technometrics, vol19#2 (1977)
  • Neural Networks in Applied Statistics, by H.S.Stern, Technometrics, vol38#3 (1996)
• Student Presentations #1
• Memorial Day - No Class
• Finals Week - Student Presentations #2; Research Paper due

These lecture notes are copyrighted by Joseph Morgan. Copyright © 2002, Joseph Morgan.