Justin Rhodes

In this course we were introduced to fundamental concepts in programming. We learned how to design algorithmic solutions to problems, analyze program code for correctness,efficiency, equivalency, and errors, as well as learning how an object-oriented methodology is implemented in program development.

The goal of this course was to investigate the essential properties of data structures and algorithms for operating on them; to use these structures as tools to assist algorithm design; to extend exposure to searching, sorting, and hashing techniques. We went over key data structures such as linked lists, hash tables, binary trees, as well as implementing graphs.

To introduce the student to the mathematical tools of logic and induction, and to the basic definitions and theorems concerning relations, functions, and sets. Later courses in the computer science curriculum build on the mathematical foundations covered here. Particular emphasis is placed on inductive definitions and proofs, with application to problems in computer science.

Provides the background in combinatorics and probability theory required in design and analysis of algorithms, in system analysis, and in other areas of computer science.

This course covers the fundamental issues in the design of modern computer systems, including the design and implementation of key hardware components such as the processor, memory, and I/O devices, and the software/hardware interface. C programming was introduced in this class, and we learned a lot about data representation, bitwise operations, cache design, and many other topics.

This course teaches essential principles, techniques, tools, and methods used to develop large software programs in Java. These include object-oriented programming and design, Unified Modeling Language (UML), testing and debugging, using and documenting APIs, asynchronous (event-driven) programming in a Graphical Use Interface (GUI) framework, code maintenance and version management using CVS, software development using Eclipse, introduction to building software on mobile platforms, introduction to multithreading and network programming, object persistence.

This course teaches students how to think about, build, debug, and test large computer programs. The course stresses learning how to use tools such as debuggers, profilers, source version control systems, and integrated development environments as an essential part of developing large programs. The course also stresses the understanding of how programs execute on today's computers and how to measure and optimize performance. Programming will be in C on Unix systems to introduce students to a new programming eco system, as well as enable the mapping of high-level language constructs to the underlying machine.

Describing and querying various forms of information such as structured data in relational databases, unstructured text (IR), semi-structured data (XML, web), deductive knowledge. Conceptual modeling and schema design. Basics of database management system services (transactions, reliability, security, optimization). Advanced topics: finding patterns in data, information mapping and integration. The course focuses on a user's perspective, rather than how one implements DBMS.

To study a variety of useful algorithms and analyze their complexity; by that experience to gain insight into principles and data-structures useful in algorithm design.

To provide a practical understanding of computer networks with respect to system architectures, protocols, and client-server interaction. These objectives are accomplished through extensive laboratory exercises.

To convey a thorough understanding of the basics of an operating system by studying techniques and algorithms for providing services in a computer system, and to understand implementation aspects of popular systems by means of case studies.

This course covers multi-variable and vector calculus. Topics include analytic geometry of three dimensions, partial derivatives, optimization techniques, multiple integrals, vectors in Euclidean space, and vector analysis.

Linear programming problems, the simplex method, duality theory, sensitivity analysis, introduction to integer programming, the transportation problem, network flows, and other applications.

Colorability, connectedness, tournaments, eulerian and hamiltonian paths, orientability, and other topics from the theory of finite linear graphs, with an emphasis on applications chosen from social, biological, computer science, and physical problems.