Lectures in PowerPoint Format

  1. Lecture 1: Intro; Using MATLAB or Python
  2. Lecture 2: Looking At Data
  3. Lecture 3: Probability and Measurement Error
  4. Lecture 4: Multivariate Distributions
  5. Lecture 5: Linear Models
  6. Lecture 6: The Principle of Least Squares
  7. Lecture 7: Prior Information
  8. Lecture 8: Solving Generalized Least Squares Problems
  9. Lecture 9: Fourier Series
  10. Lecture 10: Complex Fourier Series
  11. Lecture 11: Lessons Learned from the Fourier Transform
  12. Lecture 12: Power Spectra
  13. Lecture 13: Filter Theory
  14. Lecture 14: Applications of Filters
  15. Lecture 15: Factor Analysis and Cluster Analysis
  16. Lecture 16: Empirical Orthogonal functions and Clusters
  17. Lecture 17: Covariance and Autocorrelation
  18. Lecture 18: Cross-correlation
  19. Lecture 19: Smoothing, Correlation and Spectra
  20. Lecture 20: Coherence; Tapering and Spectral Analysis
  21. Lecture 21: Interpolation and Gaussian Process Regression
  22. Lecture 22: Linear Approximations and Non Linear Least Squares
  23. Lecture 23: Adaptable Approximations with Neural Networks
  24. Lecture 24: Hypothesis testing
  25. Lecture 25: Hypothesis Testing continued; F-Tests
  26. Lecture 26: Confidence Limits of Spectra, Bootstraps

All lectures in Zip file