This course provides an introduction to the handling, analysis, and interpretation of "big data," the massive datasets now routinely being collected in science, commerce, and government. Students achieve facility with a sophisticated, technical computing environment. The course aligns with techniques being used in several courses in the sciences, statistics, and mathematics. There are no prerequisites. The course is intended to be accessible to all students, regardless of background.

Topics course that introduces students to the field of computing by way of a central theme. Topics vary; offerings include Digital Humanities, Green Computing, and Social Media. Full description given in advance of registration. This course is suitable for students with little or no experience with computing, but it can serve as a starting point for the Computer Science major. Offered fall semester.

This course is intended to give students from diverse areas of science-e.g., economics, biology, physics, chemistry, geography, geology, mathematics, engineering, statistics-an ability to write software for solving problems and carrying out research in those disciplines. The course provides an introduction to programming and computation as well as to a number of important and widely used techniques: scientific graphics, equation solving, function fitting, optimization, storing and searching data, and simulation. There is an emphasis on ways to represent and transform information on the computer in addition to numbers and text: images, sound, graphs and databases. Offered spring semester.

This course introduces the field of computer science, including central concepts such as the design and implementation of algorithms and programs, testing and analyzing programs, the representation of information within the computer, and the role of abstraction and metaphor in computer science. The exploration of these central ideas will draw from the breadth of computer science, with an emphasis on two major application areas: multimedia processing (images, sound, and text) and robotics (control systems for autonomous robots). Course work will use the Python programming language. Every semester.

This course introduces the principles of software design and development using the object-oriented paradigm (OOP) and the Java programming language. Students will learn to use data structures such as lists, trees and hash tables and they will compare the efficiency of these data structures for a particular application. Students will learn to decompose a project using OOP principles. They will work with integrated development environments (IDEs) and version control systems. Students will practice their skills by creating applications in areas such as graphics, games, simulations, and natural language processing. There is a required 1.5 hour laboratory section associated with this course. Every semester.

Varies by semester. Consult the department or class schedule for current listing.

An in-depth introduction to the design and analysis of algorithms. Topics may include algorithmic paradigms and structures, including recursion, divide and conquer, dynamic programming, greedy methods, branch and bound, randomized, probabilistic, and parallel algorithms, non-determinism and NP completeness. Applications to searching and sorting, graphs and optimization, geometric algorithms, and transforms. Every fall.

This course builds upon the software design foundation started in COMP 124. Students will design and implement medium-sized software projects using modern software design principles such as design patterns, refactoring, fault tolerance, stream-based programming, and exception handling. The concept of a distributed computing system will be introduced, and students will develop multithreaded and networked applications using currently available software libraries. Advanced graphical user interface methods will be studied with an emphasis on appropriate human-computer interaction techniques. Students will use operating systems services and be introduced to methods of evaluating the performance of their software. Every fall.

This course familiarizes the student with the internal design and organization of computers. Topics include number systems, internal data representations, logic design, microarchitectures, the functional units of a computer system, memory, processor, and input/output structures, instruction sets and assembly language, addressing techniques, system software, and non-traditional computer architectures. Every fall.

A discussion of the basic theoretical foundations of computation as embodied in formal models and descriptions. The course will cover finite state automata, regular expressions, formal languages, Turing machines, computability and unsolvability, and the theory of computational complexity. Introduction to alternate models of computation and recursive function theory. Every spring.

Varies by semester. Consult the department or class schedule for current listing.

This course will introduce students to the design, implementation, and analysis of databases stored in database management systems (DBMS). Topics include implementation-neutral data modeling, database design, database implementation, and data analysis using relational algebra and SQL. Students will generate data models based on real-world problems, and implement a database in a state-of-the-art DBMS. Students will master complex data analysis by learning to first design database queries and then implement them in a database query language such as SQL. Advanced topics include objects in databases, indexing for improved performance, distributed databases, and data warehouses. Offered even-numbered spring semesters.

This course will examine selected topics in computational biology, including basic bioinformatics, algorithms used in genomics an genome analysis, computational techniques forsystems biology, and synthetic biology. This is an interdisciplinary course that will often be cross-listed with a course in Biology. Offered alternate spring semesters.

This course will examine the techniques that underlie compiler and interpreter creation, including lexical analysis, parsing, and compiler generators. These tools will serve as a framework for examining programming language design issues across a range of language types (procedural, object-oriented, modern programming languages with an eye to understanding the underlying philosophy of each language, and how it influences and is influenced by the needs of a compiler or interpreter for the language. "Back-end" issues, including intermediate representations, code generation, and optimization will be included as they relate to specific programming languages. Offered alternate fall semesters.

A survey of fundamental ideas and methods used in the design and construction of digital electronic circuits such as computers. Emphasis will be on applying the theoretical aspects of digital design to the actual construction of circuits in the laboratory. Topics to be covered include basic circuit theory, transistor physics, logic families (TTL, CMOS), Boolean logic principles, combinatorial design techniques, sequential logic techniques, memory circuits and timing, and applications to microprocessor and computer design. Three lectures and one three-hour laboratory per week. Offered alternate spring semesters.

The basic principles related to the design and architecture of operating systems. Concepts to be discussed include sequential and concurrent processes, synchronization and mutual exclusion, processor scheduling, time-sharing, multiprogramming, multitasking, and parallel processing. Memory management techniques. File system design. Security and protection systems. Performance evaluation. Offered alternate spring semesters.

This course will investigate the latest technology available for building web applications with dynamic content. It will look at all stages in the web application design process, including: 1) client applications, 2) web applications that service client requests, 3) application servers that manage requests for information, update data, and serve client applets, and 4) the database management system that holds the data. The course will be programming-intensive using aspects of the Java language available for designing and implementing Internet applications. The format of the course will be mainly laboratory-based sessions where you learn to build these four components of a web application, supported by lectures and discussions. Students will research particular topics and present their findings during these discussion sessions. The course will also investigate the usability of designs from a human factors standpoint and discuss privacy and other social consequences of this technology. Offered alternate fall semesters.

This course covers a central point of contact between mathematics and computer science. Many of the computational techniques important in science, commerce, and statistics are based on concepts from linear algebra: subspaces, projection, matrix decompositions, etc. The course reviews these concepts, adopts them to large scales, and applies them in the core techniques of scientific computing; solving systems of linear and nonlinear equations, approximation and statistical function estimation, optimization, interpolation, Monte Carlo techniques. Applications throughout the sciences and statistics. Every spring.

Topics in applied mathematics chosen from: cryptography; complexity theory and algorithms; integer programming; combinatorial optimization; computational number theory; applications of geometry to tilings, packings, and crystallography; applied algebra. Alternate fall semesters.

This course examines two distinct aspects of work in robotics: the physical construction of the robot's "body" and the creation of robot control programs that form the robot's "mind." It will study the strengths and weaknesses of a variety of robot sensors, including sonar, infrared, touch, GPS, and computer vision. It will also examine a variety of techniques for robot control programs, including both reactive and deliberative approaches. The course will include hands-on work with multiple robots, and a semester-long course project in robotics. The course format will be a seminar, with students reading and discussing the research literature. Offered alternate spring semesters.

Varies by semester. Consult the department or class schedule for current listing.

This course will explore how computers can analyze people's collective behavior to help them. Students will read and discuss recent academic research papers about collective behavior on sites such as Wikipedia, Facebook, and Twitter. Students will use coputational simulation and data-mining techniques to analyze online datasets. Offered alternate fall semesters.

Many current computational challenges, such as Internet search, protein folding, and data mining require the use of multiple processes running in parallel, whether on a single multiprocessor machine (parallel processing) or on multiple machines connected together on a network (distributed processing). The type of processing required to solve such problems in adequate amounts of time involves dividing the program and/or problem space into parts that can run simultaneously on many processors. In this course we will explore the various computer architectures used for this purpose and the issues involved with programming parallel solutions in such environments. Students will examine several types of problems that can benefit from parallel or distributed solutions and develop their own solutions for them. Alternate fall semesters.

An introduction to the basic principles and techniques of artificial intelligence. Topics will include specific AI techniques, a range of application areas, and connections between AI and other areas of study (i.e., philosophy, psychology). Techniques may include heuristic search, automated reasoning, machine learning, deliberative planning and behavior-based agent control. Application areas include robotics, games, knowledge representation, logic, perception, and natural language processing. Alternate fall semesters.

Working with their capstone supervisor, seminar coordinators, and other faculty, students will discuss their capstone project, make presentations of their progress, critique the work of other students, and participate in the activities of the seminar. These activities will include instruction and discussion of strategies for research, writing, and presentation. The scheduled times will include both group meetings with other seminar participants as well as individually arranged meetings with the students capstone supervisor. Every spring semester. S/NC grading only.

Varies by semester. Consult the department or class schedule for current listing.

Closely supervised individual (or very small group) study with a faculty member in which a student may explore, by way of readings, short writings, etc., an area of computer science not available through the regular offerings. Every semester.

Closely supervised individual (or very small group) study with a faculty member in which a student may explore, by way of readings, short writings, etc., an area of computer science not available through the regular offerings. Every semester.

Closely supervised individual (or very small group) study with a faculty member in which a student may explore, by way of readings, short writings, etc., an area of computer science not available through the regular offerings. Every semester.

Closely supervised individual (or very small group) study with a faculty member in which a student may explore, by way of readings, short writings, etc., an area of computer science not available through the regular offerings. Every semester.

Individual project including library research, conferences with instructor, oral and written reports on independent work in computer science. Subject matter may complement but not duplicate material covered in regular courses. Every semester.

Individual project including library research, conferences with instructor, oral and written reports on independent work in computer science. Subject matter may complement but not duplicate material covered in regular courses. Every semester.

Individual project including library research, conferences with instructor, oral and written reports on independent work in computer science. Subject matter may complement but not duplicate material covered in regular courses. Every semester.

Individual project including library research, conferences with instructor, oral and written reports on independent work in computer science. Subject matter may complement but not duplicate material covered in regular courses. Every semester.

Internships are offered only as S/D/NC grading option. Every semester.

Internships are offered only as S/D/NC grading option. Every semester.

Internships are offered only as S/D/NC grading option. Every semester.

Internships are offered only as S/D/NC grading option. Every semester.

Every semester.

Every semester.

Every semester.

Every semester.

Independent research, writing, or other preparation leading to the culmination of the senior honors project. Every semester.

Independent research, writing, or other preparation leading to the culmination of the senior honors project. Every semester.

Independent research, writing, or other preparation leading to the culmination of the senior honors project. Every semester.

Independent research, writing, or other preparation leading to the culmination of the senior honors project. Every semester.

Topics course offered for non-majors aiming to fulfill distribution requirement. Topics changes, and offerings may include Math of Elections and Voting, Climate Modeling, Game Theory, and Sports Statistics. Full descriptions given in advance of registration. Offered even-numbered fall semesters.

Epidemiology is the study of the distribution and determinants of disease and health in human populations and the application of this understanding to the solution of public health problems. Topics include measurement of disease and health, the outbreak and spread of disease, reasoning about cause and effect, analysis of risk, detection and classification, and the evaluation of trade-offs. The course is designed to fulfill and extend the professional community's consensus definition of undergraduate epidemiology. In addition to the techniques of modern epidemiology, the course emphasizes the historical evolution of ideas of causation, treatment, and prevention of disease. The course is a required component of the concentration in Community and Global Health. Every semester.

This introductory-level course focuses on mathematics useful for applied work in the natural and social sciences. There is a strong emphasis on developing scientific computing and mathematical modeling skills. The topics include differential calculus of functions of one and several variables, integration and differential equations, some introductory linear algebra, and estimation techniques. The course is an excellent preparation for Math 155, Introduction to Statistical Modeling. Case studies are drawn from varied areas, including biology, chemistry, economics, and physics. The course is designed both for students with no previous calculus, and students who have had one or two semesters of AP calculus (but who do not intend directly to take MATH 236 or MATH 237). Every semester.

An introduction to the basic techniques and methods used in combinatorial problem-solving. Includes basic counting principles, induction, logic, recurrence relations, and graph theory. Every semester.

Differentiation and integration of functions of a single variable, with applications. Main topics: Limit definition of the derivative and integral, exponential growth, chain rule, Riemann sums, numerical integration, integration by substitution and parts, improper integrals, geometric series, Taylor polynomials. This is a more in-depth course than MATH 135, and should be taken instead of MATH 135 by students intending to continue in mathematics, who have had a prior calculus course. Every semester.

An introductory statistics course with an emphasis on multivariate modeling. Topics include descriptive statistics, experiment and study design, probability, hypothesis testing, multivariate regression, single and multi-way analysis of variance, logistic regression.  Every semester.

Varies by semester. Consult the department or class schedule for current listing.

This course blends mathematical computation, theory, abstraction, and application. It starts with systems of linear equations and grows into the study of matrices, vector spaces, linear independence, dimension, matrix decompositions, linear transformations, eigenvectors, and their applications. Offered every semester.

Differentiation and integration of functions of two and three variables. Applications of these, including optimization techniques. Also includes introduction to vector calculus, with treatment of vector fields, line and surface integrals, and Green's Theorem. Every semester.

An introduction to multivariate statistical analysis. Emphasizes rationales, applications, and interpretations using advanced statistical software. Examples drawn primarily from economics, education, psychology, sociology, political science, biology and medicine. Topics may include: simple/multiple regression, one-way/two-way ANOVA, logistic regression, discriminant analysis, multivariable correlation. Additional topics may include analysis of covariance, factor analysis, cluster analysis. Every spring.

Why does 2 + 2 equal four? Can a diagram prove a mathematical truth? Is mathematics a social construction or do mathematical facts exist independently of our knowing them? Philosophy of mathematics considers these sorts of questions in an effort to understand the logical and philosophical foundations of mathematics. Topics include mathematical truth, mathematical reality, and mathematical justifications (knowledge). Typically we focus on the history of mathematics of the past 200 years, highlighting the way philosophical debates arise in mathematics itself and shape its future. Alternate years.

Varies by semester. Consult the department or class schedule for current listing.

Introduction to the theory and application of differential equations. Solving linear and first-order systems using algebra, linear algebra, and complex numbers. Using computers to solve equations both symbolically and numerically and to visualize the solutions. Qualitative methods for nonlinear dynamical systems. Applications to diverse areas of modeling.  Every semester.

Topics in modern applied statistics. Topic changes; offerings include Survival Analysis, Bayesian Statistics, Markov Chain Monte Carlo Methods. Full description given in advance of registration. Prerequisite: MATH 253 or ECON 381 or PSYC 202 or consent of instructor. Offered fall semester.

An introduction to basic probability concepts: sample spaces, probability assignments, combinatorics, conditional probability, independence, random variables, discrete and continuous distributions, functions of random variables, expectation, variance, moment-generating functions, some basic probability processes, and some fundamental limit theorems. (recommended but not required: MATH 237). Every fall.

A discussion of the basic theoretical foundations of computation as embodied in formal models and descriptions. The course will cover finite state automata, regular expressions, formal languages, Turing machines, computability and unsolvability, and the theory of computational complexity. Introduction to alternate models of computation and recursive function theory. Every spring.

This course covers a central point of contact between mathematics and computer science. Many of the computational techniques important in science, commerce, and statistics are based on concepts from linear algebra: subspaces, projection, matrix decompositions, etc. The course reviews these concepts, adopts them to large scales, and applies them in the core techniques of scientific computing; solving systems of linear and nonlinear equations, approximation and statistical function estimation, optimization, interpolation, Monte Carlo techniques. Applications throughout the sciences and statistics. Every spring.

A second course in symbolic logic which extends the methods of logic. A main purpose of this course is to study logic itself-to prove things about the system of logic learned in the introductory course. This course is thus largely logic about logic. Topics include second order logic and basic set theory; soundness, consistency and completeness of first order logic; incompleteness of arithmetic; Turing computability; modal logic; and intuitionistic logic. Alternate years.

Topics in geometry selected by the instructor. Possible courses include classical Euclidean and non-Euclidean geometry (Hilbert's axioms; parallel postulate; hyperbolic, elliptic, spherical, projective geometries; Poincare models), differential geometry (calculus on surfaces; curvature; minimal surfaces; geodesics; the Gauss-Bonet theorem), computational geometry (triangulation; point location; Voronoi diagrams; linear programming). Offered even-numbered spring semesters.

An introduction to the properties of and unsolved problems about the integers (whole numbers). This course is built around the problem of proving that a large integer is prime or finding its factorization into primes. Topics include: divisibility and prime numbers, the Euclidean algorithm, modular arithmetic, quadratic residues, continued fractions, and public-key cryptosystems. Even numbered fall semesters.

Introduction to abstract algebraic theory with emphasis on finite groups, rings, fields, constructibility, introduction to Galois theory.  Every spring.

Basic theory for the real numbers and the notions of limit, continuity, differentiation, integration, convergence, uniform convergence, and infinite series. Additional topics may include metric and normed linear spaces, point set topology, analytic number theory, Fourier series.  Every fall.

Advanced counting techniques. Topics in graph theory, combinatorics, graph algorithms, and generating functions. Applications to other areas of mathematics as well as modeling, operations research, computer science and the social sciences. Even numbered spring semesters.

Varies by semester. Consult the department or class schedule for current listing.

Draws on the student's general background in mathematics to construct models for problems arising from such diverse areas as the physical sciences, life sciences, political science, economics, and computing. Emphasis will be on the design, analysis, accuracy, and appropriateness of a model for a given problem. Case studies will be used extensively. Specific mathematical techniques will vary with the instructor and student interest. All 400-level courses will involve some independent student work such as oral presentations, papers, or computer projects. Odd numbered fall semesters.

Theory and applications of partial differential equations (PDE). Construction of PDE as models of natural phenomena. Solution via separation of variables, Fourier series and transforms, and other analytical and computational techniques. Independent or group research projects on open problems in applied PDE. Odd numbered spring semesters.

An introduction to the mathematical theory of statistics: sampling distributions, estimation, hypothesis testing, regression. Additional topics may include: analysis of variance and goodness of fit. Emphasis on the theory underlying statistic, not on applications.  Even numbered spring semesters.

Topics in applied mathematics chosen from: cryptography; complexity theory and algorithms; integer programming; combinatorial optimization; computational number theory; applications of geometry to tilings, packings, and crystallography; applied algebra. All 400-level courses will involve some independent student work such as oral presentations, papers, or computer projects. Even numbered fall semesters.

An introduction to the topology of Euclidean, metric, and abstract spaces. Covers the fundamental ideas from point set topology - continuity, convergence, and connectedness - as well as selected topics from knot theory, three-dimensional manifolds, fixed-point theory, the fundamental group, and elementary homotopy theory. Even numbered fall semesters.

Topics in algebra to be chosen from: group representations; algebraic coding theory and finite fields; Galois theory; algebraic and transcendental numbers; ring theory; applied algebra.  Alternate fall semesters. This course counts toward the capstone requirement. All 400-level courses will involve some independent student work such as oral presentations, papers, or computer projects.

A continuation of Real Analysis including discussion of basic concepts of analysis.  Topics determined by professor and may include the development of the Riemann and Lebesgue integrals, functional analysis, Fourier analysis. All 400-level courses will involve some independent student work such as oral presentations, papers, or computer projects. Odd numbered spring semesters.

Algebra of complex numbers, analytic functions, the Cauchy-Riemann equations, Cauchy's theorem, the Cauchy integral formula, Taylor and Laurent series, the residue theorem, and conformal mapping. All 400-level courses will involve some independent student work such as oral presentations, papers, or computer projects. Even numbered spring semesters.

Varies by semester. Consult the department or class schedule for current listing.

Closely supervised individual (or very small group) study with a faculty member in which a student may explore, by way of readings, short writings, etc., an area of mathematics not available through the regular offerings. Every semester.

Closely supervised individual (or very small group) study with a faculty member in which a student may explore, by way of readings, short writings, etc., an area of mathematics not available through the regular offerings. Every semester.

Closely supervised individual (or very small group) study with a faculty member in which a student may explore, by way of readings, short writings, etc., an area of mathematics not available through the regular offerings. Every semester.

Closely supervised individual (or very small group) study with a faculty member in which a student may explore, by way of readings, short writings, etc., an area of mathematics not available through the regular offerings. Every semester.

Individual project including library research, conferences with instructor, oral and written reports on independent work in mathematics. Subject matter may complement but not duplicate material covered in regular courses.

Individual project including library research, conferences with instructor, oral and written reports on independent work in mathematics. Subject matter may complement but not duplicate material covered in regular courses.

Individual project including library research, conferences with instructor, oral and written reports on independent work in mathematics. Subject matter may complement but not duplicate material covered in regular courses.

Individual project including library research, conferences with instructor, oral and written reports on independent work in mathematics. Subject matter may complement but not duplicate material covered in regular courses.

Internships are offered only as S/D/NC grading option. Every semester.

Internships are offered only as S/D/NC grading option. Every semester.

Internships are offered only as S/D/NC grading option. Every semester.

Internships are offered only as S/D/NC grading option. Every semester.

Every semester.

Every semester.

Every semester.

Every semester.

Independent research, writing, or other preparation leading to the culmination of the senior honors project. Every semester.

Independent research, writing, or other preparation leading to the culmination of the senior honors project. Every semester.

Independent research, writing, or other preparation leading to the culmination of the senior honors project. Every semester.

Independent research, writing, or other preparation leading to the culmination of the senior honors project. Every semester.