Teaching

The lectures slides for the courses taught are available for download from this web-page as a PDF.

Advanced Signal Processing

  1. Course Introduction
  2. Lecture 1: Random Variables
  3. Lecture 2: ARMA Modeling
  4. Lecture 3: Introduction to Estimation Theory
  5. Lecture 4: Minimum Variance Unbiased (MVU) Estimation
  6. Lecture 5: BLUE and Maximum Likelihood Estimation
  7. Lecture 6: Least Squares
  8. Lecture 7: Adaptive Filters

Adaptive Signal Processing and Machine Intelligence

  1. Course Introduction
  2. Lecture 1: Background Material
  3. Lecture 2: Complex gradient, noncircularity, and widely linear modelling
  4. Lecture 3: Nonparametric Spectrum Estimation
  5. Lecture 4: Modern Spectrum Estimation - Methods for Line Spectra
  6. Lecture 5: Adaptive Filters and Applications
  7. Lecture 6: Complex valued and multidimensional adaptive filters
  8. Lecture 7: Advanced Learning Systems and Neural Networks
  9. Lecture 8: Tensor Methods for Big Data Analysis
  10. Lecture 9: Feedback Adaptive Filters (not required)
  11. Lecture 10: Blind Adaptive Filters (not required)