Course Schedule
|
Week
#1
|
M
Aug 23
|
Probability
concepts, random variables
|
|
|
|
W
Aug 25
|
Random
vectors.Multivariate Gaussian.
|
|
|
Week
#2
|
M
Aug 30
|
Fundamentals
of random processes
|
|
|
|
W
Sept 1
|
Cont.
|
|
|
Week
#3
|
M
Sept 6
|
NO
CLASS (Labor Day)
|
|
|
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W
Sept 8
|
Gaussian
processes, Poisson processes, etc.
|
|
|
Week
#4
|
M
Sept 13
|
NO
CLASS
|
|
|
|
W
Sept 15
|
Markov
processes, hidden Markov models, Viterbi algorithm
|
|
|
Week
#5
|
M
Sept 20
|
Cont.
|
|
|
|
W
Sept 22
|
Moment
analysis: correlation and covariance, Karhunen-Loeve transform
|
|
|
Week
#6
|
M
Sept 27
|
Cont.
|
|
|
|
W
Sept 29
|
Frequency-domain
descriptions.Power density spectrum.White
noise.
|
|
|
Week
#7
|
M
Oct 4
|
Special
topics.Review.
|
|
|
|
W
Oct 6
|
MID-TERM
EXAM
|
|
|
Week
#8
|
M
Oct 11
|
NO
CLASS (Mid-Semester Break)
|
|
|
|
W
Oct 13
|
Random
fields.Markov random fields.Application
to image processing.
|
|
|
Week
#9
|
M
Oct 17
|
Linear
systems and random processes
|
|
|
|
W
Oct 20
|
Cont.
|
|
|
Week
#10
|
M
Oct 25*
|
Estimation:
Maximum likelihood, minimum-variance estimate
|
|
|
|
W
Oct 27*
|
Bayes
estimation: MAP, mean-square, and linear mean-square
|
|
|
Week
#11
|
M
Nov 1
|
Cont.
|
|
|
|
W
Nov 3
|
Linear
prediction
|
|
|
Week
#12
|
M
Nov 8
|
Wiener
and Kalman filtering
|
|
|
|
W
Nov 10
|
Cont.
|
Seminars
|
|
Week
#13
|
M
Nov 15
|
Cont.
|
|
|
|
W
Nov 17
|
Spectrum
estimation
|
|
|
Week
#14
|
M
Nov 22
|
Complex
random processes and vector random processes
|
|
|
|
W
Nov 24
|
NO
CLASS (Thanksgiving Break)
|
|
|
Week
#15
|
M
Nov 29
|
Special
topics.Review.
|
|
|
|
W
Dec 1
|
FINAL
EXAM
|
|