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 |
