The lecture slides will be placed on this website after each lecture.
Need more help or you have comments you would like to share? Why not email me. (For help do so well in advance of the exam.) pdf]
Least Mean Square Algorithm [ pdf]
Recursive Least Squares [ pdf]
Kalman Filter [ pdf]
Hidden Markov Model [ pdf] Examples paper: part 1 [ pdf], part 2 [ pdf]
(Examples class is second half of lecture 7 and lecture 8)
Solutions: part 1 [ pdf], part 2 [ pdf] Lec2_SDexample.m
Matlab m-file for Lecture 3: Noise Cancellation problem [ noiseCancel.m], Learning AR signal parameters [ misAdjustment.m], NLMS [ nlms.m]
For noise cancellation, you should plot the various signals. Also consider the effect of changing the filter order, the frequency of both the noises, their lags etc.
Matlab m-file for Lecture 4: Learning AR signal parameters using the RLS [ rlsExample.m] algorithm
The Matlab file for Examples Sheet 1, Q4 [ ex1Question4.m]
The Matlab file implementing the scalar KF
The Matlab file for Dishonest Casino pdf]
Periodogram properties [ pdf]
Improving the Periodogram [ pdf]
Parametric methods [ pdf]
Fitting the MA model [ pdf]
Maximum likelihood for ARMA model estimation [ pdf] Examples paper with solutions [ pdf] Periodogram Same demo for periodogram_white_noise.m
Matlab demo of validity of the approximation of the variance of the Periodogram for a more general wide sense stationary process Periodogram variance
Matlab demo for ARMA MLE
Matlab file for Question 17 of Spectrum estimation Examples paper.