CAP 5510/CGS 5166: Introduction to Bioinformatics

If you have wondered what the field of Bioinformatics is all about, then this is your chance to learn. If you thought that you need a lot of biology background to master the field of Bioinformatics, then it is time to rethink that assumption. Learn to use your knowledge of algorithms, databases, internet programming, data mining, machine learning, information retrieval, statistics, artificial intelligence, and much more, and apply it to the burgeoning field of Bioinformatics. This is an introductory course in Bioinformatics that has been taught since 2003 at FIU. The course has matured over the years and we cover a lot of ground, from introductory material to advanced research material. Over the years this course has had students from different disciplines and has leveraged the interdisciplinary synergy. Learn how to work with fellow students from the life sciences.

COT 6936: Topics in "BIG DATA" Algorithms

The focus of this course will be on "applying" theoretical algorithms and smart data structures to problems in areas of computing that involve massive datasets. This course is offered only once every two years. Prerequisites are minimal (COT 5407 or or its equivalent (COT 5407) is a prerequisite. The course will cover material from three books available freely online and a variety of sources, notes and research papers. This course is geared toward research and is primarily targeted toward CS graduate students (PhD and MS students) with requisite background and passion. Several research publications have originated from past course projects (Spring 2010, 2012, 2014).

The course material will be chosen from the following topics, but with a major focus on "Big Data Algorithms":

  • Approximation algorithms
  • Advanced Graph Algorithms
  • Randomized algorithms and analysis (Markov chains, random walks)
  • On-line algorithms (paging algorithms and server problem algorithms)
  • Streaming algorithms
  • Experimental Algorithmics
  • Combinatorial optimization, linear programming and duality
  • Metric embeddings
  • Spectral partitioning
  • Power-aware computing
  • Algorithmic game theory
  • Parallel algorithms
  • Burrows-Wheeler transforms, compressed full text indexes
  • Computational Biology and Bioinformatics
  • Computational Geometry
  • Big Data Analytics
  • Adaptive Algorithms

Additional information