Syllabus: 
syllabus in pdf syllabus in html 

Logistics: 
TTh 11:0012:30
GDC 4.302 Unique Number: 50959 Course web page: http://www.cs.utexas.edu/~diz/378 

Professor:  David Zuckerman Email: diz@cs.utexas.edu Phone: 4719729 Office: GDC 4.508 Office Hours: M 23, W 12. 

TA:  Xue Chen Email: xchen@cs.utexas.edu Office Hours: Tu 1011, F 12, TA Station of GDC. 

Who should take this?  Students who like theory, probability, and algorithms. This course is excellent preparation for graduate school.  
Text: 
Mitzenmacher and Upfal, Probability and Computing Errata 

Course Overview: 
Randomness is extremely useful in computer science.
Algorithms that make random choices during their execution, known as randomized algorithms, are often faster or simpler than algorithms that don't use randomness.
Examples include Quicksort, primality testing, and Monte Carlo simulations.
However, such randomized algorithms usually come with a small probability of error, so it is important to bound this error probability.
In this undergraduate course, we develop tools and techniques
to design and analyze efficient randomized algorithms.
This course is theoretical and mathematical; there will be no programming
assignments.
Each section of the course is built around a method, with example
applications to randomized algorithms.
We list the topics below.


Prerequisites:  Computer Science 331 or 331H or 357 or 357H. This means that you need the prerequisites and corequisites for CS 331, including Discrete Math (CS 311 or 311H), Probability (SDS 321 or M 362K), and Linear Algebra (SDS 329C, Math 340L, or Math 341).  
Canvas:  We will use Canvas, which contains Piazza. Homeworks and grades will be posted on Canvas. We will use Piazza for class discussion. Instead of emailing questions to the teaching staff, please post your question to Piazza.  
Useful Pointers: 
My essay
Can Random Coin Flips Speed Up a Computer? Theory of Computing Blog Aggregator 