Courses

Techniques for design and analysis of algorithms; efficient algorithms for sorting, searching, graphs, and string matching; and design techniques such as divide-and-conquer, recursive backtracking, dynamic programming, and greedy algorithms.

Investigation of various properties of algorithms and their corresponding data structures, such as lists, stacks, queues trees, and B-trees. Some emphasis on comparable efficiencies of these tools. Languages that may be used for these types of applications include Java or C++.

Project oriented hands-on approach to accompany CS 303. CS 330. Computer Organization and Assembly Language Programming. 3 Hours. Register-level architecture of modern digital computer systems, digital logic, machine-level representation of data, assembly-level machine organization, and alternative architectures.

Algorithms for standard computational problems; design and implementation of efficient algorithms; mathematical analysis of algorithm efficiency.

Survey of design and analysis of efficient algorithms. Introduces methods of describing algorithm time and space complexity and various problem-solving techniques.

Hashing, Bloom filters and applications, Cuckoo filters, quotient filters, streaming algorithms, heavy hitters and count-min sketch, other sketching algorithms, external memory algorithms and I/O efficiency, dimensionality reduction, coresets, MAPReduce. Programming projects.

Introduction to algorithms as tools for computational l problem solving, language of algorithms, understanding algorithms, approximately analyzing correctness and efficiency of algorithms, algorithms that solve fundamental computational problems, basic algorithm design techniques, steps of computational problem solving.

Advanced complexity analysis techniques, notions of computational complexity, polynomial time hierarchy, computability, algorithms that solve advanced computational problems, advanced algorithm design techniques, computational problem solving.

This course covers the design and analysis of algorithmic techniques applied in bioinformatics. Topics include sequence comparison, alignment and matching, suffix tree, sequence database search, phylogenetic tree, genome rearrangement, motif finding, RNA prediction, and peptide sequencing.

This course covers the design and analysis of algorithmic techniques applied in bioinformatics. Topics include sequence comparison, alignment and matching, suffix tree, sequence database search, phylogenetic tree, genome rearrangement, motif finding, RNA prediction, and peptide sequencing.