Science and Engineering
SUNY at Buffalo
CSE 694: Topics in Algorithms - Probabilistic Analysis and Randomized AlgorithmsInstructor: Hung Q. Ngo
|Teaching Staff||Contact and Administrative Information|
|Hung Q. Ngo (instructor)||
Office: 238 Bell Hall
Probabilistic analysis and randomized algorithms have become an indispensible tool in virtually all areas of Computer Science, ranging from combinatorial optimization, machine learning, approximation algorithms analysis and designs, complexity theory, coding theory, to communication networks and secure protocols. This course has two major objectives: (a) it introduces key concepts, tools and techniques from probability theory which are often employed in the development of probabilistic algorithms and analyses and in the constructions of combinatorial objects, and (b) it gives a glance of how these concepts and techniques are used in algorithm analysis and design and combinatorial constructions, among other usages.
In addition to the probabilistic paradigm, students are expected to gain substantial discrete mathematics problem solving skills essential for computer engineers.
Basic Algorithm course (CSE 531), a good sense of discrete mathematics thinking, and rudimentary knowledge of probability theory.
The desire and ability to learn new ideas quickly.
At the end of this course, each student should be able to: