6.011 | Spring 2018 | Undergraduate
Signals, Systems and Inference

Calendar

Ses # TopicS Key Dates
Signals & Systems in Time & Frequency
1 Introduction Recitation 1
2 Transforms Recitation 2
3 Energy Spectral Density

Recitation 3

Problem Set 1 Due

State-Space Models
4 State-Space Models  
5 State-Space Models, Equilibrium, Linearization

Recitation 4 

Problem Set 2 Due

Modal Solutions of LTI Systems, Reachability and Observability, Transfer Functions, Hidden Modes
6 Modal Solution of Undriven CT and DT LTI State-Space Models Recitation 5
7 Full Modal Solution, Asymptotic Stability, Reachability, and Observability

Recitation 6

Problem Set 3 Due

8 Matrix Exponential, ZIR+ZSR, Transfer Function, Hidden Modes, Reaching Target States Recitation 7
Observers, State Feedback, Observer-Based Feedback
9 Observers for State Estimation

Recitation 8 

Problem Set 4 Due

10 Observers, State Feedback Recitation 9
11 State Feedback, Observer-Based Feedback

Quiz 1

Probablistic Models, Random Variables
12 Probabilistic Models, Random Variables Recitation 10
MMSE and LMMSE Estimation
13 Vector Picture for First- and Second-Order Statistics; MMSE and LMMSE Estimation

Recitation 11 

Problem Set 5 Due

14 LMMSE Estimation, Orthogonality Recitation 12
15 Normal Equations, Random Processes

Recitation 13

Problem Set 6 Due

WSS Processes
16 Wide-Sense Stationary Processes; LTI Filtering of WSS Processes Recitation 14
17 LTI Filtering of WSS Processes

Recitation 15

Problem Set 7 Due

18 Power Spectral Density (PSD)

Recitation 16

Problem Set 8 Due

Wiener Filtering
19 Einstein-Wiener-Khinchin Theorem, PSD Applications, Modeling Filters

Recitation 17

Recitation 18

Quiz 2

20 Wiener Filtering

Recitation 19 

21 Wiener Filtering Illustrations

Recitation 20

Problem Set 9 Due

Hypothesis Testing
22 Hypothesis Testing Recitation 21 
Signal Detection
23 Neyman-Pearson Testing, Signal Detection

Recitation 22

Problem Set 10 Due

24 Matched Filtering

Recitation 23

Recitation 24

Problem Set 11 Due

Final Exam (during finals week)

Learning Resource Types
assignment Problem Sets
grading Exams
notes Lecture Notes
co_present Instructor Insights