This course provides the basic sequence and structural bioinformatics as well as introduction to bioinformatic algorithms.

This course provides an introduction to some of the fundamental techniques and principles of neural computation, including the basic models, structures and learning algorithms.

After this course, a student should be able to know how to use ANNs for solving various problems related to pattern recognition, function approaximation, dimensionality reduction and data visualization.

After this course, a student should be able to know how to use ANNs for solving various problems related to pattern recognition, function approaximation, dimensionality reduction and data visualization.

This course is an introduction to data mining that focuses on matrix methods and features real-world applications ranging
from classication and clustering to dimensionality reduction and data visualiation.
Mathematical topics covered include: linear equations, regression, the singular value decomposition, and iterative algorithms.
Students are expected to have taken a course in calculus and have exposure to numerical computing (e.g. Matlab, Python, R).

This course provides an introduction to the fundamentals of multimedia systems. understand the components of multimedia including images and
videos. Different types and formats for images are also discussed in this course.

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