**References**

#### Probablistic Method & Pseudo-randomness

- To be updated.

#### Algorithmic Coding Theory & Applications

**Books**:- Essential Coding Theory - A book being written by Venkat Guruswami, Madhu Sudan and Atri Rudra
- Introduction to Coding Theory by J. H. van Lint. Springer-Verlag, Berlin, 1999. (only for the initial lectures)

**Lecture Notes**:- Lecture Notes from last editions of this course. (This is a dropbox link to the file which is being edited as you are reading this).
- Notes from a Fall 2006 course taught be Venkat Guruswami at U. Washington.
- The excellent notes from Madhu Sudan's Fall 2001 course at MIT.
- Notes from Atri Rudra's course at Univ. at Buffalo.

**Surveys**: Here are some surveys that provide a more computer science view:- L. Trevisan, Some Applications of Coding Theory in Computational Complexity, Quaderni di Matematica 13:347-424, 2004.
- A. Rudra, Algorithmic Coding theory, Book Chapter, CRC Handbook on Algorithms and Theory of Computation.
- V. Guruswami, Algorithmic results for list decoding, Foundations and Trends in Theoretical Computer Science, Volume 2, Issue 2, 2007.
- V. Guruswami, Error-correcting codes and Expander graphs, SIGACT News, 35(3), 2004.

#### Fourier Analysis & Applications

- A book being written by Ryan O'Donnel (The lectures will mainly be based on this book)
- Noise sensitivity of Boolean functions and percolation a book written by Christophe Garban and Jerey E. Steif (relevant chapters on Fourier analysis on the Boolean cube are useful for this par of the course)
- Survey on applications of Fourier analysis to Learning theory by Y. Mansour.