This course covers computational aspects of comparative genomic analysis,
an important tool used
to investigate cutting-edge questions
in biology and human health.
A primary goal of the course is to introduce quantitative foundations
that underly this area and, more generally, bioinformatics and computational biology.
This course covers advanced topics in artificial intelligence (AI) and intelligent systems.
Major topics will include: (1) representation, (2) inference, (3) learning, (4) applications, and (5) other
current research topics.
A fundamental emphasis in the course will be dealing with uncertainty using probabilistic approaches.
Course work will consist of both theory and practice.
In this course, students will survey
fundamental data structures and many associated algorithms.
Emphasis will be placed on matching the appropriate data
structures and algorithms to application problems.
Analysis of algorithms is
crucial to making proper selections,
so analysis is important in the course.
This course assumes that students are already familiar with advanced programming
techniques, including the definition of classes, and use of dynamic memory and
linked data structures, including lists and trees. Even though the treatment of
algorithms and data structures is mostly conceptual, students are expected to
be able to transform these algorithms and data structures into programs
through effective software module development.