Calendar

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