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