Computer Science

CS 1000 Engineering Computing (Credits: 3) Co-requisite: CS 1010, MATH 1210. Course taught: Fall, Spring, Every year.

Introduction to programming principles and engineering problem solving via computational means using MATLAB (during the first half of the semester) and C (during the second half of the semester). Decomposition of programs into data representation, functions, and control structures. Clean programming practices are emphasized. The MATLAB portion of the course focuses on the implementation of physically-based models, data visualization via plotting and selected numerical techniques. The C portion of the course introduces basic syntax and special features of the language for engineering implementations.

CS 1020 Introduction to Programming in C++ (Credits:  3)

An introduction to essential programming concepts using C++. Laboratory practice required.

CS 1021 Introduction to Programming in Java (Credits: 3) Course taught: Fall. Every year.

An introduction to essential programming concepts using Java. Laboratory practice emphasizes object-oriented techniques and web-based application design.

CS 1410 Introduction to Computer Science I (Credits: 4) Co-requisite: CS 1010, MATH 1210. Course taught: Fall semester, every year.  

The first course required for students intending to major in computer science. Introduction to the engineering and mathematical skills required to effectively program computers, and to the range of issues confronted by computer scientists. Roles of procedural and data abstraction in decomposing programs into manageable pieces. Introduction to object-oriented programming.  Extensive programming exercises that involve the application of elementary software engineering techniques.

CS 2000 Introduction to Programming Design in C (Credits: 4) Co-requisite: CS 1010, MATH 1210. Course taught: Fall, Spring, Every year.

Introduction to essential programming concepts using C. Decomposition of programs into functional units; control structures; fundamental data structures of C; recursion; dynamic memory management; low-level programming. Some exposure to C++. Laboratory practice. (Intended for non-CS/CE majors).

CS 3500/5010 Software Practice (Credits: 4) Prerequisites: CS 2420. Course taught: Fall, Every year. CS 5010 is for graduate students from departments other than Computer Science.

Practical exposure to the process of creating large software systems, including requirements specifications, design, implementation, testing, and maintenance. Emphasis on software process, software tools (debuggers, profilers, source code repositories, test harnesses), software engineering techniques (time management, code, and documentation standards, source code management, object-oriented analysis and design), and team development practice. Much of the work will be in groups and will involve modifying preexisting software systems.

CS 4150 Algorithms (Credits: 3) Prerequisites: CS 2100, 2420. 

Study of algorithms, data structures,a nd complexity analysis beyond the introductory treatment from CS 2420. Balanced trees, heaps, hash labels, string matching, graph algorithms, external sorting and searching. Dynamic programming, exhaustive search. Space and time complexity, derivation and solution of recurrence relations, complexity hierarchies, reducibility, NP completeness. Laboratory practice.

CS 5150/6150 Advanced Algorithms (Credits: 3) Prerequisites: CS 4150. Course taught: Spring, Every year.

Design and analysis of algorithms. Greedy algorithms, dynamic programming, divide and conquer. Asymptotic analysis and recurrence relations. Graph algorithms and network flows. Computational complexity and intractability. NP-hardness and beyond. Approximation algorithms.

CS 5530/6530 Database Systems (Credits: 3) Prerequisites: CS 3500. Course taught: Fall, Every year.

Representing information about real world enterprises using important data models including the entity-relationship, relational and object-oriented approaches. Database design criteria, including normalization and integrity constraints. Implementation techniques using commercial database management system software. Selected advanced topics such as distributed, temporal, active, and multi-media databases.

CS 5600 Introduction to Computer Graphics (Credits: 3) Prerequisites: CS 3500, MATH 2250. Basic display techniques, display devices, and graphics systems. Homogeneous coordinates, transformations, and clipping. Introduction to lighting models. Introduction to raster graphics and hidden-surface removal.

CS 5610/6610 Interactive Computer Graphics (Credits: 3) Prerequisite: CS 5600. 

Interactive 3D computer graphics, polygonal representations of 3-D objects. Interactive lighting models. Introduction to interactive texture mapping, shadow generation, image-based techniques such as stencils, hidden-line removal, and silhouette edges. Introduction to image-based rendering, global illumination, and volume rendering.

CS 5630/6630 Scientific Visualization (Credits: 3) Prerequisites: CS 3505; CS 3200 or CS 6210 or MATH 5600. Course taught: every third semester beginning in Fall 1999.

Introduction to the techniques and tools needed for the visual display of data. Students will explore many aspects of visualization, using a "from concepts to results" format. The course begins with an overview of the important issues involved in visualization, continues through an overview of graphics tools relating to visualization, and ends with instruction in the utilization and customization of a variety of scientific visualization software packages.

Geography

GEOG 3020 Geographical Analysis (Credits: 3) Prerequisite: MATH 1030, 1050 or equivalent. Course taught: Spring, every year.

Emphasizes the spatial point of view and presents techniques of spatial analysis applicable to all fields of geography. Introduction to the use of multiple correlation and regression techniques in geographic research with special attention addressing problems in the use of these techniques with spatial data.

GEOG 3040 Principles of Cartography (Credits: 4). Prerequisite: MATH 1030, 1050 or equivalent. Course taught: Fall, every year.

Fundamental principles of cartography including perception, visualization, topographic and thematic map interpretation, field mapping techniques (including GPS), and creating computer-based maps in weekly labs. Principles include direction, scale, grids, projections, and spatial transformations, spatial data analysis, data manipulation decisions, color theory and application, and principles of cartographic design and critical evaluation.

GEOG 3110 The Earth from Space: Remote Sensing of the Environment (Credits: 3). Course taught: Fall, every year. 

Over the past decade there has been an extraordinary increase in the availability of remote sensing images of Earth. Many people are now familiar with programs like Google Earth. The explosion in the availability of remote sensing data has coincided with a growing number of remote sensing applications. Remote sensing data are now used in anthropology, civil engineering, environmental sciences, geography, geology, hydrology, natural resource assessment, meteorology, and urban planning. This course adopts an interdisciplinary approach applicable to those fields, examining remote sensing theory, techniques, and applications. The course explores the physical basis for remote sensing and remote sending technologies that use sunlight, infrared radiation, radar, and lasers. Five lab exercises give "hands-on" experience with real remote sensing data.

GEOG 3140 Introduction to GIS (Credits: 3) Prerequisite: MATH 1030, 1050 or equivalent. Course taught: Fall, every year. 

A recent increase in the use of digital geographic information in many fields has created the need for experts with the knowledge to use this information to society's benefit. Geographers, engineers, environmental scientists, planners, social scientists, computer scientists and many other professionals will encounter digital geographic information in some form in their future careers. This course introduces students to issues that arise in using this information in scientific and decision-making arenas. Topics include: applications of geographic information; modeling geographic reality; spatial data collection; geographic analysis; accuracy and uncertainty; visualization; and legal, economic, and ethical issues associated with the use of geographic information.

GEOG 5110 Environmental Analysis Through Remote Sensing (Credits: 3) Prerequisite: GEOG 3110.  Course taught: Spring, every year. 

High-resolution multispectral data, coupled with expanding computing power and increasingly sophisticated image processing software, provides a large set of quantitative, graphic and science visualization tools for solving science-based environmental problems using remote sensing data. The theory and application of image-processing techniques such as: data corrections, enhancements, transformations, and classification are aimed at specific environmental problems in the natural and human domains. Hands-on experience is gained through image processing laboratory techniques, field-based measurements and real-world science projects.

GEOG 5120 Environmental Optics (Credits: 3) Prerequisites: GEOG 3110; MATH 1060 or PHYS 1010 or equivalencies; or instructor consent.  Course taught: Spring, every year. 

The physical principles that determine how light and matter interact are essential to understanding remote sensing and Earth's energy budget. This course explores the complex interactions of electromagnetic radiation with the Earth's surface and atmosphere from a quantitative perspective. The physical foundations of visible, infrared, and microwave remote sensing are addressed using both theory and laboratory measurements. Theoretical explanations of reflection, absorption, and transmission of electromagnetic radiation are used to explore practical applications of environmental optics in remote sensing, climate modeling, and everyday phenomena.

GEOG 5130 Advanced Remote Sensing Applications (Credits: 3) Prerequisite: GEOG 5110.  Course taught: Fall, every year. 

Project-based science applications; project objectives, selection of alternative procedures, planning, execution, evaluation, and publication.

GEOG 5140/6140 Methods in GIS (Credits: 4) Prerequisite: GEOG 3140 (except for graduate students). Course taught: Spring, every year. Graduate students should enroll in GEOG 6140 and will be held to higher standards and/or more work.

This course explores the practice of using a geographic information system (GIS) to support geographic inquiry and decision making. Students will strengthen their technical knowledge of the common tasks that a geographic analyst faces in applying a GIS to a variety of spatial problems. The lab sections offer an opportunity to gain hands-on experience using a leading commercial GIS to complete a series of real-world projects.

GEOG 5150/6150 Spatial Database Design for GIS (Credits: 4) Prerequisite: GEOG 5140/6140. Course taught: Fall, every year. Graduate students should enroll in GEOG 6150 and will be held to higher standards and/or more work.

Digital spatial data is widespread due to the global positioning system (GPS), satellite-based remote sensing, intelligent transportation systems and other geographic information technologies. Spatial data is important and useful due to geographic information systems (GIS) and other spatial applications such as Internet map serving and location-based services. However, spatial data involves complex objects and relationships that cannot be accommodated easily by standard database management systems. This course reviews the fundamentals of database design and data management to support GIS and other spatial applications. Topics include modeling spatial data, spatial database design, spatial query languages, spatial database storage and indexing, and spatial query optimization.

GEOG 5160/6160 Spatial Modeling with GIS (Credits: 3) Prerequisite: GEOG 5140/6140. Course taught: Spring, every year.  Graduate students should enroll in GEOG 6160 and will be held to higher standards and/or more work.

The power to model complex environmental systems in a geo-spatial framework is one of the great assets of GIS. This course places the fundamental operations and software of spatial analysis and GIS in a modeling framework. The course addresses advanced concepts and techniques in map algebra, cartographic modeling and descriptive and predictive spatial modeling. The course has both lecture and required lab components.