| L1 |
Overview; Problem Review; Random Vectors |
PS 1 Out |
| R1 |
Course Information; Review of Linear Algebra |
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| L2 |
Covariance Matrices; Gaussian Variables |
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| L3 |
Gaussian Vectors; Bayesian Hypothesis Testing |
PS 1 Due
PS 2 Out |
| R2 |
Diagonalization of Symmetric Matrices; Symmetric Positive Definite and Semidefinite Matrices |
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| L4 |
Binary Hypothesis Testing; ROCs |
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| R3 |
More on Symmetric Positive Definite Matrices; Hypothesis Testing for Gaussian Random Vectors |
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| L5 |
ROCs; M-ary Hypothesis Testing |
PS 2 Due
PS 3 Out |
| L6 |
Bayesian Estimation; LS; MAP |
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| R4 |
Binary Hypothesis Tests: Receiver Operating Characteristic (ROC); Geometry of M-ary Hypothesis Tests |
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| L7 |
Bayes and Linear LS |
PS 3 Due
PS 4 Out |
| L8 |
Vector Spaces |
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| R5 |
Bayes' Least Squares Estimation; Vector Spaces and Linear Least Squares |
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| L9 |
Nonrandom Parameter Estimation CRB |
PS 4 Due
PS 5 Out |
| L10 |
ML Estimation |
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| R6 |
Nonrandom Parameter Estimation |
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| L11 |
QUIZ #1 (through Lecture 8, PS# 1-4) |
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| L12 |
Stochastic Processes |
PS 5 Due
PS 6 Out |
| R7 |
Linear Systems Review |
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| L13 |
Second-Order Descriptions |
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| L14 |
PSD's |
PS 6 Due
PS 7 Out |
| R8 |
Examples of Stochastic Processes; Second Order Statistics and Stochastic Processes |
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| L15 |
Whitening, Shaping; K-L |
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| L16 |
K-L; Freq, Domain Representation |
PS 7 Due
PS 8 Out |
| R9 |
Discrete Time Processes and Linear Systems; Discrete Time Karhunen–Loeve Expansion |
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| L17 |
Detection and Estimation in White Noise |
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| L18 |
Nonlinear Estimation |
PS 8 Due
PS 9 Out |
| R10 |
Binary Detection in White Gaussian Noise; Detection and Estimation in Colored Gaussian Noise |
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| L19 |
Det/estimation in Colored Noise; LLSE of Processes |
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| R11 |
Linear Detection from Continuous Time Processes; Karhunen–Loeve Expansions and Whitening Filters |
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| L20 |
QUIZ #2 (through Lecture 16, PS# 5-8) |
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| L21 |
Wiener Filtering |
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| R12 |
Discrete–Time Wiener Filtering; Prediction and Smoothing |
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| L22 |
Innovations, State Models |
PS 9 Due
PS 10 Out |
| L23 |
Kalman Filtering |
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| R13 |
State Space Models and Kalman Filtering |
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| L24 |
KF; Estimation of Statistics |
PS 10 Due
PS 11 Out |
| L25 |
Estimation of Statistics; Modeling |
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| R14 |
Estimation and Detection Using Periodograms |
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| L26 |
Modeling |
|
|
Final Exam |
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