A survey of mathematics used to solve current problems, including analysis of political and social structures, problem solving, and abstraction. Topics may include voting methods, power distributions, apportionment, fair division, graph theory, coding theory, scheduling, personal finance, geometry/fractals and statistics.

Covers domain, graphs, inverses, and compositions of functions, with particular attention given to exponential, logarithmic, and trigonometric functions. Emphasizes understanding and manipulating functions analytically, graphically, and numerically. Intended for students who wish to take MATH 130 Calculus I or CHEM 110 General Chemistry and are placed in MATH 120. Students who already have credit for MATH 130/140/230/260 are not allowed to take this course without instructor permission.

This course is concerned with the fundamental ideas of calculus: the derivative as a rate of change, the integral as an accumulation of change, and the limit as the basic idea underlying both the derivative and the integral. Numerical, geometric, and analytic approaches to these ideas are emphasized throughout the course. Using the techniques of calculus to solve real-world problems is fundamental to the course. Students are introduced to partial derivatives and mathematics software. Students are expected to have prior familiarity with exponential, logarithmic, trigonometric, and rational functions.

This course continues the study of calculus begun in MATH 130. Primary goals of the course are solving initial value problems, learning standard techniques of integration (integration by parts, trigonometric substitution, partial fractions), becoming familiar with Taylor polynomial approximations and Taylor series expansions of standard functions, and employing these ideas in real-world applications. Numerical, geometric, and analytic approaches to the fundamental ideas of calculus are emphasized throughout the course. Graphing calculators and mathematics software are fundamental tools. Prerequisite: MATH 130 or advanced placement

An introduction to some of the mathematical and statistical methods used in the analysis of social and natural scientific phenomena with an emphasis on the interpretation of experimental and survey data. Topics include elementary and combinatorial designs, basic statistical methods, correlation and inference, and regression analysis. Applications to the students’ major disciplines are included throughout the course as well as in one or more substantial projects. This course does not satisfy any requirements for a major or minor in mathematics. Students may not receive credit for both this course and another introductory statistics course such as BUSI 250, PSYC 290, or SOCI 210.

Vectors and coordinate systems in two and three dimensions, vector-valued functions, functions of several variables, extrema, multiple integrals, vector fields, including fundamental theorems of vector calculus. Emphasis is placed on developing geometric intuition. Prerequisite: MATH 140

An introduction to the discrete paradigm in mathematics and computer science. Topics include induction, recursion, logic, algorithmic problem-solving, asymptotic analysis of algorithms, graph theory, number theory, and counting techniques.

Study of ordinary differential equations and systems of equations, through the use of analytic, qualitative/geometric, and numerical techniques. Applications from physics, biology, chemistry, engineering, economics, and psychology are presented. Prerequisite: MATH 140

Solving linear systems, matrix algebra, vector spaces and linear transformations, eigenvectors, orthogonality. Prerequisite: MATH 130

Fundamentals of set theory, logic, and functions. Emphasis is on developing the students’ theorem-proving skills, independent work, written and oral communication skills, and ability to critique others’ work. Prerequisite: MATH 140 and completion of, or concurrent enrollment in, MATH 240 or MATH 270

Theory of probability and mathematical statistics including an introduction to basic concepts of probability theory, discrete and continuous random variables, distribution theory, moment-generating functions, and the Central Limit Theorem. Other topics may include the theory of statistical inference, point estimation, confidence intervals, regression, hypothesis testing, and analysis of variance. Offered in alternate years. Prerequisite: MATH 140

Introduction to classical algebraic systems and their morphisms. Topics include groups, rings, fields, substructures, ideals, homomorphisms, and quotients. Offered in alternate years. Prerequisite: MATH 290

An investigation of various geometries. Topics include axiomatic systems, formal Euclidean geometry, non-Euclidean geometries such as spherical, hyperbolic, and projective, as well as geometric transformations. Prerequisites: MATH 240 or MATH 290

A rigorous study of the structure of the real line and the properties of real-valued functions. Topics include sequences, limits, continuity, differentiability, and integrability. Offered in alternate years. Prerequisite: MATH 290

Methods used to model, gather, interpret, and evaluate data critically. Topics may include model building, signal processing, numerical analysis, and stochastic processes. Offered in alternate years. Prerequisite: Any MATH course numbered 230 or higher

Algebraic topics that extend the fundamental ideas in MATH 320 will be presented. Offered in alternate years. Prerequisite: MATH 320

Analytic topics that extend the fundamental ideas in MATH 350 will be presented. Offered in alternate years. Prerequisite: MATH 350

A topics course in mathematics. This course can be repeated for additional credits as long as the section topics are different. Recent section topics include Game Theory, Image Processing, Topology, Number Theory, Complex Variables. Consult the online course schedule for information about the topics currently scheduled to be taught. Prerequisite: Consent of instructor

A required seminar for all senior mathematics majors which meets throughout the academic year. Each student develops an individual research project under the direction of a faculty advisor and presents the results both orally and in written form.

Introduction to computational problem-solving and computer programming. Topics include imperative programming constructs (variables, loops, conditionals, functions, recursion, file processing), basic object-oriented constructs (classes, objects), and some fundamental algorithms and data structures (dictionaries, arrays, linked lists, regular expressions). Students learn through studying the Python programming language. Laboratory course.

Builds on skills acquired in CSCI 150, placing emphasis on object-oriented software design and data abstraction. Students are introduced to data structures (lists, stacks, queues, trees, heaps, hash tables, graphs) and programming techniques such as recursion and sorting algorithms. Other topics include analysis of algorithm complexity, automated unit testing, inheritance. Programming assignments focus on the design and implementation of algorithms and data structures using the Java language. Laboratory course. Prerequisite: CSCI 150 or consent of instructor

A study of the application of artificial intelligence to solving problems in robotics. Topics include subsumption, planning, machine learning, vision, neural networks, localization, and mapping. Students configure provided implementations of algorithms to control physical robots. Prerequisite: CSCI 150

A survey of the tools and techniques of computation as applied to concepts in humanities. Covers the use of computing to analyze and synthesize textual, visual, and aural data, as well as the creation of new digital artifacts using computation. Topics normally include natural language processing and translation, information retrieval, sentiment analysis, document clustering, data visualization, procedural music generation, and digital art. Prerequisite: CSCI 150

Students visualize scientific data from a wide variety of sources, perform data analysis and dimensionality reduction using clustering techniques, understand system dynamics through differential equation approximation and agent-based modeling, construct probabilistic Monte-Carlo simulation models, and optimize continuous and discrete problems with local search techniques. Topics may include Markov models, bioinformatics, and signal processing. Each assignment requires writing a clear and coherent data analysis report that properly characterizes legitimate conclusions to be drawn from the data. Prerequisite: MATH 130 and CSCI 150

A study of the three major concepts of a modern operating systems: virtualization, concurrency and persistence. Topics include the memory hierarchy (including caching and virtual memory), memory management, processes, processor scheduling, address spaces, threads, the critical section problem, locks, sockets, file systems, and system performance analysis. Prerequisites: CSCI 151

A study of the layers of abstraction composing the design of modern computing systems. Topics include numeric representation, digital logic, memory circuits, CPU design, machine and assembly language, the program stack, virtual machines, compilers, assemblers, memory management, and memory-mapped I/O. Prerequisite: CSCI 151

In this course, students implement and employ machine learning and search algorithms to create software to solve problems in three major application areas of Artificial Intelligence: action selection, natural language processing, and image classification. Writing assignments emphasize careful scientific analysis of student-conducted AI experiments and analytical thinking about AI applications. Prerequisite: CSCI 151

A study of designing and using a database management system (DBMS) and of developing Web applications. Topics include HTML, CSS, the JavaScript language, relational database theory, techniques for supporting ACID properties, and frontiers in database research. As part of a large team, students design and develop a system using both Web and mobile front ends that interacts with a DBMS using SQL. Prerequisite: CSCI 151

Introduction to the computer science concepts necessary for the development of software systems targeted at mobile devices. Further exploration of object-oriented development, and testing concepts introduced in CSCI 151. Emphasis is paced on user-centric interface design and writing precise requirements. Projects also incorporate network programming and mobile device sensor data. Assignments emphasize the integration of multiple concepts when developing realistic software applications. Several projects are completed in teams. Prerequisite: CSCI 151

In this hands-on course, students develop programming language design and implementation skills through a number of examples, using the Haskell progamming language (no prior experience with Haskell is assumed). Topics covered include parsing, type checking, first-class functions, interpreters, abstraction, compositionality, embedded domain-specific languages. As a final project, each student creates and implements a domain-specific language of their own design. Prerequisite: CSCI 151

An in-depth, hands-on introduction to functional programming in Haskell. Topics include algebraic data types and pattern matching, recursion, induction, folds, lambda calculus, laziness, functions, and monads. The last few weeks of the semester focus on computer-checked formal proofs, exploring the deep connection between functional programs and formal logic. Prerequisites: CSCI 150 and one of MATH 240 or MATH 290

An introduction to contemporary tools and algorithms for building interactive games. Students will learn fundamental design mechanics and implement a substantial development project. Topics may include steering and flocking behavior, path finding algorithms, finite state machines, behavior trees, alpha-beta pruning, Monte Carlo Tree Search, shaders, 3D modeling, animation, procedural content generation, and the intersection of games and society. Prerequisite: CSCI 151

Covers basic topics in automata, computability, and complexity theory, including: models of computation (finite automata, Turing machines and RAMs); regular sets and expressions; recursive, recursively enumerable, and non-recursively enumerable sets and their basic closure properties; complexity classes; determinism vs. non-determinism, with and without resource bounds; reductions and completeness; practice with NP- and P-completeness proofs; and the complexity of optimization and approximation problems. Prerequisite: MATH 240

Introduction to algorithm design strategies that build upon data structures and programming techniques introduced in earlier courses. Strategies discussed include brute-force, divide-and-conquer, dynamic programming, problem reduction, and greedy algorithms. Topics covered include graph traversal and shortest paths, string matching, searching, sorting, and advanced data structures such as balanced search trees, heaps, hash tables, state machines, and union-find structures. The course includes an introduction to complexity theory and the complexity classes P and NP. Prerequisites: CSCI 151 and MATH 240

In this course intended for computer science minors, the student completes a semester-long project investigating the relationship of the student's major with computing. Typically, this involves developing software to solve a computational problem in the major discipline. This course must be taken as an independent study, supervised by a computer science faculty member in consultation with a faculty member in the student's major discipline. Prerequisite: CSCI 151

A combination of readings, writing assignments, oral presentations, and independent project work integrates the lessons from each student's undergraduate studies. Students create and revise project documentation, assess the content of formal writing about computing subjects, investigate ethical issues in computing, and complete an independent capstone project. Students also prepare themselves for professional work by resume writing and the creation of a professional portfolio. Prerequisite: CSCI 151

In this course, students support the missions and workflows of community partners by developing and deploying software industry tools and practices. Projects include interactive websites that organize and share information to manage complex systems, mobile applications to connect users with resources and information, and educational games to promote empathy and learning. Instructor approval is required prior to enrollment. A student receives one course credit after two semesters of participation. Students can retake this course for a total of two course credits. Prerequisites: CSCI 340, CSCI 352, or CSCI 370, and permission of instructor

A topics course in computer science. This course can be repeated for additional credits as long as the section topics are different. Recent section topics include Interactive Game Development, Functional Programming, Artificial Intelligence, Distributed Computing. Consult the online course schedule for information about the topics currently scheduled to be taught. Prerequisite: Consent of instructor