Pattern Recognition
 
  1. Introduction to Pattern Recognition
  2. Review of Statistics and Probability
  3. Linear algebra and MATLAB
  4. Bayesian Decision Theory
  5. Quadratic Classifiers
  6. Parameter Estimation
  7. Kernel Density Estimation
  8. Nearest Neighbors
  9. Principal Components
  10. Fisher Linear Discriminants
  11. Sequential Feature Selection
  12. Randomized Feature Selection
  13. Cross-validation
  14. Mixture Models and EM
  15. Statistical Clustering
  16. Competitive Learning
  17. Linear Discriminant Functions
  18. Multilayer Perceptrons
  19. Radial Basis Functions
  20. MLPs, RBFs and Statistical PR
  21. Support Vector Machines
  22. SVMs and Kernel Methods
  23. Discrete HMMs, Viterbi
  24. Baum-Welch and Entropic Training
  25. Ensemble Learning
  26. Manifold Learning
  27. Independent Components Analysis
  28. Kernel PCA/LDA
 
Presentations/Invited Talks
 
  1. Pattern recognition for chemical sensor arrays with neuromorphic models of the olfactory system (GOSPEL 2005)
  2. Principal Discriminants Analysis for small-sample-size problems: application to chemical sensing (SENSORS 2004)
  3. Cancellation of chemical backgrounds with generalized Fisher’s linear discriminants (SENSORS 2004)
  4. Statistical classifiers: Bayesian decision theory and density estimation (NOSE 2004)
  5. Coherent Oscillations as a Neural Code in a Model of the Olfactory System (IJCNN 2003)
  6. Signal Processing Methods for Drift Compensation (NOSE 2003)
  7. A Self-organizing Model of Chemotopic Convergence for Olfactory Coding (EMBS 2002)
  8. Transient Response Analysis for Temperature Modulated Chemoresistors (IMCS 2002)
  9. Multi-Frequency Temperature Modulation for Metal-Oxide Gas Sensors (ISOEN 2001)
  10. Statistical Pattern Recognition (NOSE 2000)
 
Intelligent Sensor Systems
 
  1. Course introduction
  2. Sensor characteristics
  3. Survey of sensing principles
  4. Sensor interface circuits
  5. The ideal op-amp
  6. Data acquisition I
  7. LABVIEW examples
  8. Data acquisition II
  9. Introduction to pattern analysis
  10. Dimensionality reduction
  11. Linear algebra and MATLAB
  12. Classification
  13. Validation
  14. Intelligent sensor systems
  15. ISS communications
 
Microprocessor-Based System Design (68000)
 
  1. Course introduction
  2. MC 68000 architecture
  3. MC68000 instruction set
  4. Addressing modes
  5. Program control
  6. Subroutines I
  7. Subroutines and stack frames
  8. C language
  9. Exception processing
  10. PI/T timer
  11. PI/T parallel I/O, part I
  12. PI/T parallel I/O, part II
  13. DUART serial I/O, part I
  14. DUART serial I/O, part II
  15. Memory and I/O interface
  16. Address decoding
 

 
PRISM | Computer Science | Dwight Look CoE | TAMU