A general introduction to computer science including an overview of hardware systems, programming essentials, algorithm design, data handling, and networking. Not intended for students needing a programming background for further work in computer science.

An introduction to tools of computer science that are relevant to bioinformatics, with a focus on fundamental problems with sequence data. Practical topics will include Perl programming, data management, and web services. Computational concepts are emphasized with only a sketch of the underlying biology.

An introduction to tools and concepts of computer science that are most relevant to business (enterprise) computing and e-commerce. Students will be introduced to basic programming principles, page layout and visual interface design, client/server computing, simple techniques for accessing databases, and their algorithmic and mathematical foundations.

An introduction to computer science for the student who expects to make serious use of the computer in course work or research. Topics include: fundamental computing concepts, general programming principles, the Linux Operating System, the X-window system, and the Java programming language. Emphasis will be on algorithm development with examples highlighting topics in data structures.

Emphasis is on the use and implementation of data structures, introductory algorithm analysis, and object oriented design and programming with Java.

Emphasis is on the use and implementation of data structures, introductory algorithm analysis, and object oriented design and programming with Java.  For students who have not had CS 170 at Emory, but have an extensive programming background or AP credit.

Topics will be announced each year.

This course introduces elementary mathematics necessary for the computer science curriculum. Topics include proof-writing, sets, functions, logic, quantifiers, graphs, automata, languages, and asymptotic notation.

C programming. Elementary CPU and computer architecture. Data representation. Binary, octal and hexadecimal number systems. ASCII and binary representation and conversion. Assembly language programming, with emphasis on how data structures and program constructs in C are represented in Assembler. Data structures: struct, array and list. Program constructs: if, while, procedure call and return, and recursion

Rotating topics in computer science. May be repeated for credit when the topic varies. Pre and co requisites depend on the topic offered.

Analysis, design, and implementation of data structures and algorithms. Algorithms include divide and-conquer, dynamic programming, greedy methods, tree and graph traversal, with analysis emphasizing lower bounds, worst-case, and expected time complexity.

Foundations and problems of machine intelligence, application areas, representation of knowledge, constraint processing, AI programming languages, expert systems, design of an intelligent system.

This course will focus on the analysis of syntactic and semantic structures, ontologies and taxonomies, distributional semantics and discourse, as well as their applications in computational linguistics. Assignments will include advanced programming implementations.

Digital circuits, efficient algorithms for computer arithmetic, floating point accelerators, micro-programming, memory technology and hierarchies, I/O subsystems, interrupt processing and DMA strategies, communications interfaces, and advanced architectures, including RISC and cache organization.

Introduction to syntax and semantics of computer programming languages. An overview of various language paradigms with case studies in declarative languages, object-oriented languages, and logic programming as contrasted with imperative languages. An overview of translation issues and methods.

This course introduces basic concepts and techniques of software engineering, and applies these in the context of a semester-long group programming project.

Prerequisite: permission of instructor. Credit, variable. An independent study course devoted to the development of software projects. Cannot be used to meet course requirements for a CS major.

Introduction to storage hierarchies, database models, consistency, reliability, and security issues. Query languages and their implementations, efficiency considerations, and compression and encoding techniques.

Introduction to data mining techniques including data preprocessing, data warehousing and management, association analysis, clustering, and text mining.

Rotating topics in computer science. May be repeated for credit when the topic varies. Pre and co requisites depend on the topic offered.

This course gives mathematical methods to classify the complexity of computational problems. Topics include regular languages, grammars, decidability, and NP-completeness. Models of computing such as automata, circuits, and Turing machines are related.

System programming topics are illustrated by the POSIX API to the Linux operating system. Topics include: file i/o, the TTY driver, window systems, processes, shared memory, message passing, semaphores, signals, and interrupt handlers.

The structure and organization of computer operating systems. Process, memory, and I/O management; device drivers, exception handling, and interprocess communication. Students write an operating system as a course-long project.

Understanding offense is key to better cyberdefense. We focus on advanced vulnerabilities, exploits and defense technologies. We teach the hacker mindset, ethics as well as C and assembly.

An introduction to computer networks based on internal structure using the OSI layer model. Topics include: physical layer (encoding and protection), data link layer (point-to-point and broadcast networks, transparent bridging, and spanning tree), the network layer (routing algorithms, the IP protocol, tunneling), and transport layer (UDP and TCP protocols, NS2 network simulation). Network programming will be done using the Berkeley socket and pthreads APIs.

Languages and their grammars, lexical analysis and parsing, code generation, and optimization. Functional and Logic programming. Evaluation will include the design and implementation of a semester-long compiler project for a simple imperative language.

May be repeated for credit when topic varies. Pre/co-requisites vary with topic.

May be repeated for credit when topic varies. Pre/co-requisites vary with topic.

Prerequisite: consent of instructor. Cannot be used to meet course requirements for a CS major or minor.

Convex sets, linear inequalities, linear programming, two-person games, finite graphs. Applications in management, economics, and behavioral sciences.

Limits, continuity, derivatives, antiderivatives, the definite integral.

Fall. Limits, continuity, derivatives, antiderivatives and definite integrals; applications to optimization, physical and life science models. Lab includes web-based practice and evaluation.

Techniques of integration, exponential and logarithm functions, sequences and series, polar coordinates.

Topics include: techniques of integration, exponential and logarithm functions, sequences and series, and polar coordinates.

Integration, differential equations, multivariable calculus, and discrete probability and statistics, with an emphasis on applications to biology.

Development and use of mathematical models from probability and statistics with applications.

Vectors; multivariable functions; partial derivatives; multiple integrals; vector and scalar fields; Green's and Stokes' theorems; divergence theorem.

This is a standard first semester Differential Equations course which covers first and second-order differential equations and systems of differential equations, with an emphasis placed on developing techniques for solving differential equations.

Systems of linear equations, matrices, determinants, linear transformations, eigenvalues and eigenvectors, least squares.

An introduction to theoretical mathematics. Logic and proofs, operations on sets, induction, relations, functions.

Topics in the history of mathematics and their philosophical background. Genesis and evolution of ideas in analysis, algebra, geometry, mechanics, foundations. Historical and philosophical aspects of concepts of infinity, mathematical rigor, probability, etc. The emergence of mathematical schools.

Topics in the history of mathematics and their philosophical background. Genesis and evolution of ideas in analysis, algebra, geometry, mechanics, foundations. Historical and philosophical aspects of concepts of infinity, mathematical rigor, probability, etc. The emergence of mathematical schools.

This course is the first half of the advanced math introductory sequence. It covers the basics of linear algebra: vector spaces, linear transformations, determinants, and eigenvalues, with an emphasis on mathematical rigor. This class is for freshmen who scored a 5 on the Calculus AP BC exam.

This course is the second half of the advanced mathematics introductory sequence. It covers the basics of vector calculus: differentiable mappings, differential forms, and integration theory.

Solution of linear and nonlinear systems of equations, interpolation, least-squares approximation, numerical integration, and differentiation.

Analytic functions, elementary functions, integrals, power series, residues, and conformal mapping.

Axiomatic treatment of vector spaces, inner product spaces, minimal polynomials, Cayley Hamilton theorem, Jordan form, and bilinear forms.

This course introduces the basic concepts of algebraic and analytic number theory. Topics include: congruence relations, the distribution of prime numbers, quadratic reciprocity, Diophantine equations, continued fractions, and generating functions.

Combinations and permutations, counting techniques, recurrence relations, and generating functions. Block designs, finite planes, and coding theory. Introduction to graph theory.

Curves and surfaces in 3-space. The geometry of the Gauss map. Special surfaces. The intrinsic geometry of surfaces. Surfaces and computer graphics.

Principles of mathematical modeling; case studies using nonlinear ordinary differential equations, difference equations, and partial differential equations.

Theory of linear programming, duality, optimal flows in networks, and mathematical programming.

Nonlinear optimization problems arise in a wide range of applications, for example, in economics, physics, engineering, imaging. This introductory course covers a wide range of examples and both theory and practice of unconstrained and constrained optimization.

PDEs and their origin, classification of PDEs, analytical methods for the solution of PDEs, qualitative properties of the solutions, eigenvalue problems and introduction to numerical methods.

Partial Differential Equations (PDE's) are a formidable tool for describing real-life problems. In this course we use PDE's for cardiovascular problems and other real-life situations. Students will visit radiology labs and learn about image processing and numerical simulations in medicine.

Finite and continuous probability theory, distribution models (binomial, geometric, uniform, normal, Poisson, and exponential), the Chebyshev inequality, expectation and variance, moment generating functions, the central limit theorem, and applications.

Fundamentals of Statistical Inference: estimation, properties of estimators, methods for comparing estimators, confidence intervals, hypothesis testing, regression, and analysis of variance.

Rotating topics in mathematics. May be repeated for credit when the topic varies. Pre and co requisites depend on the topic offered.

Analysis of sets and functions in n-space which focuses on basic topological properties of sets as well as continuity and differentiation of functions. Topics: exterme value theorem, chain rule, and inverse function theorem. Emphasis will be placed on rigorous proof and intution, not computation

This course is a continuation of Math 411 which focuses on integration and uniform convergence in n-space. Topics include: Stoke's theorem, Fubini's theorem, Taylor's theorem, the Stone-Weierstrass theorem, and Sard's theorem.

Groups (definition and examples), cosets, Lagrange's Theorem, symmetric and alternating groups, Cayley's Theorem, isomorphisms, Cauchy's Theorem, quotient groups and homomorphisms, and the action of a group on a set. Additional topics may include the Sylow Theorems.

Ring Theory and Field Theory: polynomial rings, unique factorization, Euclidean domains, splitting fields of polynomials, elements of Galois theory, finite fields.

Introduction to the use of calculus in economic analysis; comparative static problem and optimization theory; consideration of the mathematical techniques used in game theory.

Rotating topics in mathematics. May be repeated for credit when the topic varies. Pre and co requisites depend on the topic offered.

May be repeated for credit when topic varies.

May be repeated for credit when topic varies.

May be repeated for credit when topic varies.

May be repeated for credit when topic varies.

May be repeated for credit when topic varies.

May be repeated for credit when topic varies.

May be repeated for credit when topic varies.

Normally taken in student's last semester, up to a maximum of 4 credit hours.

Normally taken in student's last semester, up to a maximum of 4 credit hours.

May be repeated for credit, total credit not to exceed six hours. Cannot be used to meet course requirements for a Math major or minor