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I am teaching Advanced Signal Processing in January 2014. Here are the details. It is tentatively a 13 week course with 2 lectures per week. The detailed schedule is as follows:

Lecture No.

Contents

1

Some Fundamentals of Functional Analysis

2

Linear Inverse Problems and Introduction to Least Squares

3

A Linear Algebraic Look at Least Squares

4

Regularization and Applications of Least Squares

5

Solving Least Squares Problems (Algorithms)

6

Under-determined Linear Inverse Problems and Compressed Sensing

7

Compressed Sensing: Applications

8

Compressed Sensing: Algorithms - greedy methods

9

Compressed Sensing: Algorithms - optimization based

10

Group-sparsity, row-sparsity and other variants of Compressed Sensing

11

Low-rank Matrix Recovery: Theory

12

Low-rank Matrix Recovery: Applications

13

Low-rank Matrix Recovery: Algorithms

14

Low-rank Matrix Recovery: Algorithms (contd.)

15

Combining Sparsity with Rank Deficiency

16

Student Paper Presentation

17

Student Paper Presentation (contd.)

18

ARMA Parameter Estimation

19

Kalman Filtering

20

MLE and MAP estimation

21

Markov Chains

22

Hidden Markov Model

23

Student Project Presentation

24

Student Project Presentation (contd.)

25

Student Project Presentation (contd.)

26

Student Project Presentation (contd.)

 

Evaluation

20% - Class Tests

20% - Home Assignments

10% - Mid Terms

40% - Final Project

10% - Final Exam