Information Technology - Intelligent System

List of Courses

CSBP301
CSBP400
 CSBP411
CSBP421
CSBP431
 CSBP441
CSBP461
CSBP499
 ITPG602
ITPG630
ITPG676


* All credit hours are based on the current term, this may vary for previous terms.

CSBP301 - Artificial Intelligence (3 credit hours)
Principles and methods for knowledge representation, reasoning, learning, problem solving, planning, heuristic search, natural language processing, speech recogition; LISP, PROLOG, or expert system programming languages. Pre-requisite: ITBP319.
Prerequisite:
  1. ITBP319
Corequisite:
Semester: Fall Spring Go To Index


CSBP400 - Modeling & simulation (3 credit hours)
Introduction to System Modeling and Decision-Making using computer simulation. A wide range of case studies are discussed. Discrete-Event Simulation and the popular modeling paradigms. Continuous and Hybrid simulations, Input modeling, Output analysis, Random numbers, as well as application areas and tools for simulation. (Prerequisite: STAT210)
Prerequisite:
  1. STAT210
Corequisite:
Semester: Fall Spring Go To Index


CSBP411 - Machine Learning (3 credit hours)
Adaptive technologies used in Speech and Image Processing; Bioinformatics systems; Web search and text classification; Feature extraction; Decision trees; Neural networks; Genetic algorithms; Bayesian learning; Reinforcement Learning.
Prerequisite:
  1. CSBP301
Corequisite:
Semester: Fall Spring Go To Index


CSBP421 - Smart Computer Graphics (3 credit hours)
Fundamental techniques in graphics, Graphic systems. Graphic communication Geometric modeling. Basic and Advanced Rendering Techniques, Computer animation. Visualization, Virtual reality, 3D Computer Games. (Prerequisite:ITBP319).
Prerequisite:
  1. ITBP319
Corequisite:
Semester: Fall Spring Go To Index


CSBP431 - Bioinformatics (3 credit hours)
Overview of molecular biology as related to bioinformatics. Bioinformatics and the relationship between computer science and biology in the field of bioinformatics. Algorithms in general and specifically those often used in bioinformatics. Computing tools used in bioinformatics. Databases available for bioinformatics work. Scientific method and how bioinformatics applications apply. Models of successful collaborations between biologists and computer scientists. Computational models of biological processes and their role in scientific discovery.
Prerequisite:
  1. ITBP319
Corequisite:
Semester: Fall Spring Go To Index


CSBP441 - Applied Computer Vision (3 credit hours)
Fundamentals of computer vision: Pattern recognition concepts; Low-level (early) visual processing; Color histogram for object recognition and tracking; Optical flow motion vector estimation for video surveillance and sequence processing; Stereo analysis for range to object estimation; Disparity estimation for 3D object reconstruction.
Prerequisite:
  1. CSBP421
  2. CSBP301
Corequisite:
Semester: Fall Spring Go To Index


CSBP461 - Internet Computing (3 credit hours)
Web Technologies (HTTP Protocol, Presentation abstractions, Web-markup and display languages, Client-side programming, Server-side programming); Web services and servers; Emerging technologies. Information Architecture (Hypertext/hypermedia, Web design); Digital Media; Web Development (interfaces, database access); Social Web software.
Prerequisite:
  1. ITBP340
Corequisite:
Semester: Fall Spring Go To Index


CSBP499 - Special Topics in Int. Sys. (3 credit hours)
Advanced and emerging topics of special interest to undergraduates; May be repeated once with a substantially different topic. To be taken in final semester of senior year.

Prerequisite:
  1. ITBP315
Corequisite:
Semester: All Go To Index


ITPG602 - Research Methods in IT (3 credit hours)
Techniques and conventions in research methods, evaluation approaches, Ethics, and presentation of results, how to choose a research topic, how to write a thesis proposal or a research proposal, common research methods in IT, research outcomes presentation, research evaluation, research papers review.
Prerequisite:
Corequisite:
Semester: Fall Spring Go To Index


ITPG630 - Pattern Recognition (3 credit hours)
This course introduces the fundamentals of statistical pattern recognition with examples from several application areas. Techniques for analyzing multidimensional data of various types and scales along with algorithms for projection, dimensionality reduction, clustering and classification of data are explained. The course presents competing approaches to exploratory data analysis and classifier design, allowing students to make judicious choices when confronted with real pattern recognition problems. Students use MATLAB software and implement some algorithms using their choice of a programming language. Topics include: Bayes decision theory, parametric approaches, the Ugly Duckling theorem, discriminant functions, performance assessment, nonparametric classification, feature extraction, unsupervised learning, support vector machines and kernels, and Boosting basics.
Prerequisite:
Corequisite:
Semester: All Go To Index


ITPG676 - Intelligent Agents & Semantic (3 credit hours)
Semantic web, reactive and deductive agents, reasoning on the web, agent communication techniques, ontologies, social web systems, semantic web-based services.
Prerequisite:
Corequisite:
Semester: All Go To Index