CSCI 121: Introduction to Computer Science
This course introduces students to computer science and its applications. Students explore fundamental concepts, including common data structures, conditionals, iteration, functions, recursion, and introductory object-oriented software design. Students apply these concepts using the Python programming language in hands-on projects relevant to fields such as the arts, humanities, social sciences, and natural sciences. No prior programming experience is expected or required. Offered each semester. Either CSCI 121 or CSCI 251 counts toward applied linguistics concentration.
OLE Core Requirement: Quantitative and Computational Reasoning.
CSCI 200: Topics in Computer Science
Students explore special topics in computer science. Topics may vary from year-to-year. May be repeated if topics are different. Offered periodically.
Prerequisite: permission of instructor.
CSCI 221: Introduction to Data Structures in C++
This course introduces students to the C++ programming language and common data structures. Students develop their understanding of how machines store and operate on data, down to the individual bits. Students learn about storage space and run-time requirements of common data structures, including stacks, queues, linked lists, and trees. Combined with their deeper understanding of how the computer processes data, students analyze programs for efficiency and to apply the right data structure for specific scenarios. Offered each semester.
Prerequisite: CSCI 121 or CSCI 125 or PHYS 130 or permission of instructor.
CSCI 241: Hardware Design
This course explores computer hardware and how the code we write uses it, taught through in-class labs and daily assignments. Topics include an introduction to computer systems, computer organization and architecture, the component-level design of computer hardware, forms of parallelism, machine-level implementation of programming language features, memory organization, logic circuits, data representation, assembly programming, and a brief introduction to networking and cloud computing. Offered each semester.
Prerequisite: CSCI 221 or permission of instructor.
CSCI 251: Software Design and Implementation
This course provides an introduction to the structure and creation of computer software, using the C++ programming language and emphasizing object-oriented programming and structured collaborative software-development methodology. Concepts and skills are applied in a substantial multi-week team project. Topics include object-oriented programming, specification, programmed memory management, indirect addressing, tools including team software process, software design strategies, and elementary ethical analysis of software systems. Offered each semester.
Prerequisite: CSCI 221 or permission of instructor.
CSCI 263: Ethical Issues in Software Design
The software we design has real effects in people's lives. This course explores the ethical and social considerations inherent in computer-based systems, develops skills in thinking about those considerations and in collecting data to determine their effects, and expands students' abilities to integrate these issues and skills into software development procedures. Coursework uses case studies and surveys topics such as professional and ethical responsibilities, risk, liability, intellectual property, privacy, computer crime, and AI ethics. Offered each semester.
Prerequisite: CSCI 251 or permission of instructor.
OLE Core Requirement: Ethical Reasoning in Context; OLE Experience in Practice.
CSCI 273: Operating Systems
This course examines the features of modern operating systems, including detailed consideration of Linux and other example systems. Projects range from system-level programming and multithreaded network programming to kernel modifications. Topics include operating system principles, implementation as system calls, process scheduling and dispatch, concurrency, inter-process communication, programming with threads and sockets, low-level memory management, device management, file systems, security and protection mechanisms, virtual machines, and kernel programming. Offered periodically.
Prerequisites: completion of or concurrent enrollment in CSCI 241 and CSCI 251, or permission of instructor.
CSCI 276: Programming Languages
The course begins with a survey of several popular programming languages, learning to write some code in each of them. We then consider important language features that have been used to describe entire categories of languages. Topics include programming language semantics, programming language translation, parsing, memory structures, abstraction mechanisms, and language translation systems and types. Also counts toward applied linguistics concentration.
Prerequisites: CSCI 251 or permission of instructor.
CSCI 279: Introduction to Machine Learning
Machine learning algorithms are used to make decisions in a wide variety of applications, including recommendation systems, criminal justice, healthcare, and more. This course explores several common machine learning algorithms: how they work, their strengths and limitations, their applications to real-world datasets, their implementation via Python frameworks, and the ethical and societal consequences of their use. A background in statistics or data science is recommended but not required. Offered annually.
Prerequisites: CSCI 221 or permission of instructor.
CSCI 294: Academic Internship
This is an intermediate-level version of CSCI 394.
OLE Core Requirement: OLE Experience in Practice.
CSCI 298: Independent Study
Independent work is guided by a faculty member. Students need to have the necessary background, including appropriate coursework, before an independent project is approved. A student may not substitute an IS/IR course for a course regularly offered in a department or program. IS/IR culminates in at least one product (a paper, laboratory report, work of art, etc.). Normally, the student and faculty instructor are expected to meet at least once a week. See catalog for more information under registration and course enrollment.
Prerequisites: at least second-year standing; at least 2 relevant courses for 298 (IS); approval from the faculty instructor, the academic advisor, and the department chair or program director.
CSCI 300: Topics in Computer Science
Recent and planned topics include parallel and distributed computing, mobile computer graphics, and relational database systems. May be repeated if topic is different. Offered periodically.
CSCI 333: Theory of Computation
Students learn about formal languages, automata, and other topics concerned with the theoretical basis and limitations of computation. The course covers automata theory including regular languages and context-free languages, computability theory, complexity theory including classes P and NP, and cryptographic algorithms. Offered periodically.
Prerequisite: a proof writing course (such as MATH 234, MATH 244, or MATH 252) and computer science major or permission of instructor.
CSCI 353: Analysis of Algorithms
This course surveys standard topics in the study of algorithms, with an emphasis on complexity analysis and implementation experience. Topics include asymptotic analysis, searching and sorting, divide and conquer, basic graph algorithms, greedy algorithms, dynamic programming, and NP-complete problems. Offered each semester.
Prerequisites: MATH 234 and CSCI 251, or consent of the instructor.
CSCI 379: Foundations of Artificial Intelligence
Artificial intelligence is an extremely broad field in which the overarching goal is the creation of an autonomous agent with human-level capabilities. Students study the fundamental algorithms and techniques used to create agents with varying degrees of autonomy and function, including search algorithms, first-order logic, Bayesian networks and simple neural networks. Students learn how to represent problems for specific techniques, and how to select the best technique for a given problem. Offered annually.
Prerequisites: CSCI 251 and MATH 234, or permission of instructor.
CSCI 390: Senior Capstone Seminar
Class members participate in undergraduate research, including readings from the research literature, team development of project software, ethical analysis of their project applying CSCI 263 principles, documentation practices, and writing a research paper for public presentation. Offered annually.
Prerequisites: major in computer science with senior status, and completion of or concurrent enrollment in computer science core courses, ordinarily including CSCI 353 and CSCI 263, or permission of instructor.
CSCI 391: Ghana International Capstone (study abroad)
Class members participate in a team research project abroad. The central activity of the project teams is to implement an applied research project in collaboration with a host community in West Africa. This research project drives the other elements of the course, which include readings from the research literature, ethical analysis of their project applying CSCI 263 principles, documentation practices, and writing a research paper for public presentation. Offered periodically during January Term. Apply through the Smith Center for Global Engagement.
Prerequisites: computer science major with junior or senior status and completion of computer science core courses, ordinarily including CSCI 353 and CSCI 263, or permission of instructor.
OLE Core Requirement: OLE Experience in Practice.
CSCI 394: Academic Internship
Students gain experience in computer-industry positions. Recent projects have included contributions to team programming, documentation, business applications of computing, modifications of large proprietary code bases using industry-standard software frameworks, test-driven development, creating mobile-computing interfaces for existing software systems, and improving security of company websites. Internship experiences (whether for credit or not) are strongly encouraged for anyone considering a career in computing.
OLE Core Requirement: OLE Experience in Practice.
CSCI 396: Directed Undergraduate Research
This course provides a comprehensive research opportunity, including an introduction to relevant background material, technical instruction, identification of a meaningful project, and data collection. The topic is determined by the faculty member in charge of the course and may relate to their research interests. Offered based on department decision. May be offered as a 1.00 credit course or .50 credit course.
Prerequisite: determined by individual instructor.
CSCI 398: Independent Research
Recent projects, usually executed by individuals, but occasionally in small groups, and coordinated with ongoing undergraduate research projects, include cluster-assisted computer vision for robots, parallel computing in computer science education, and middleware for interdisciplinary web applications.
MATH 220: Elementary Linear Algebra
This course beautifully illustrates the nature of mathematics as a blend of technique, theory, abstraction, and applications. The important problem of solving systems of linear equations leads to the study of matrix algebra, determinants, vector spaces, bases and dimension, linear transformations, and eigenvalues. Offered each semester and during January term.
Prerequisite: MATH 119, MATH 120 or CSCI 221, or with permission of the Mathematics Placement Director.
OLE Core Requirement: Quantitative and Computational Reasoning.
MATH 234: Discrete Mathematical Reasoning
In this course students use topics in discrete mathematics to engage in problem solving, abstract mathematical thinking, and logical reasoning. Students practice proof writing in multiple contexts, including propositional and predicate logic; the foundations of set theory, functions, and relations; elementary number theory; arithmetic and geometric progressions; permutations, combinations, and basic probability; and graph theory. Offered most semesters. May not be taken after MATH 244 or MATH 252, except with instructor permission.
Prerequisite: CSCI 221, MATH 126 or MATH 128.
MATH 261: Computational Geometry
Computational geometry is the study of algorithms for solving geometric problems. This course introduces students to concepts of discrete geometry and their use in geometric algorithms. Topics may include polygons and polyhedra, triangulations, convex hulls, Voronoi diagrams, and configuration spaces. This course combines both theoretical and computational perspectives, and offers applications to areas such as computer graphics, mesh generation, robotics, and geographic information systems. Offered alternate years during January term.
Prerequisites: MATH 242 or MATH 244 or MATH 252 or (MATH 234 and CSCI 221), or instructor permission.
PHYS 246: Electronics
Modern scientific work relies heavily on electronic circuitry and computation. This course examines the fundamentals of analog and digital electronics, explores the applications of discrete and integrated circuits, and introduces the broad topic of computer control of experiments. Students develop hands-on skills in circuit building, computer interfacing, and programming in LabView®. Students attend lectures and one 2.5-hour laboratory per week. Offered alternate years.
Prerequisite: PHYS 125 or PHYS 131.
SDS 333: High-Dimensional Data Analysis
Data with thousands of dimensions or columns is commonplace across multiple disciplines, including image analysis, natural language processing, genomics, time series, and recommendation systems. In this class students learn, understand, and apply principles and techniques to gain insights from high-dimensional data. Topics include clustering, principal component analysis, multidimensional scaling, non-negative matrix factorization, missing-value imputation for recommendation systems, topic modeling, word embeddings, object recognition using "eigenfaces", non-linear embedding techniques, multiple hypothesis testing, and false discovery rate adjustments. Offered periodically during fall and spring semesters.
Prerequisites: MATH 220 and SDS 164.
SDS 341: Algorithms for Decision Making
This course introduces students to the subject of machine learning. The primary focus is the development and application of powerful machine learning algorithms applied to complex, real-world data. Topics covered include linear regression, nearest neighbor models, k-means clustering, shrinkage methods, decision trees and forests, boosting, bagging, support vector machines, and hierarchical clustering. Applications are taken from a wide variety of disciplines, including biology, economics, public policy, public health, and sports. Offered annually during fall and spring semester.
Prerequisite: SDS 264 and SDS 272, or permission of the instructor.
