Computer Science (CS)

A set of courses exploring interesting applications of computing in a variety of disciplines. These courses are primarily intended for students who wish to satisfy the QR requirement and enhance their abilities to apply computing to the solution of quantitative problems.

A set of courses exploring interesting applications of computing in a variety of disciplines. These courses are primarily intended for students who wish to satisfy the QR requirement and enhance their abilities to apply computing to the solution of quantitative problems. Courses are offered periodically depending on faculty availability.

A set of courses exploring interesting applications of computing in a variety of disciplines. These courses are primarily intended for students who wish to satisfy the QR requirement and enhance their abilities to apply computing to the solution of quantitative problems. Courses are offered periodically depending on faculty availability.

An introduction to the principles of design, implementation, and testing of computer programs. The course covers language features such as control structures, classes, file I/0, and basic data structures including arrays. Other topics include recursion and fundamental algorithms, such as elementary searching and sorting algorithms. May not be taken for credit by students who have taken or are currently taking CS 107, CS 206, CS 209 or CS 226.

An introduction to computer science. Students will develop simple 2D games, animations and simulations using the Java programming language and Processing, a Java package designed to make programming accessible to visual artists. The course introduces the fundamental techniques of computer programming, software design, and algorithm development.

Fundamentals of software development and algorithm design. Topics include recursion, data structures, analysis of algorithms, and program verification.

An introduction to data structures and the mathematical foundations of algorithms. Properly organizing the data within a software application is critical to the efficiency of the application’s execution as well as the ease with which the application is developed. This course will introduce the fundamental data structures used in computer science, including linked lists, stacks, queues, priority queues, search trees, and hash tables. In addition, the course will introduce foundational discrete mathematical concepts used in the design and analysis of algorithms, including mathematical induction, asymptotic analysis and recurrence relations.

An introduction to Knowledge Discovery and Data Mining (KDD), also known as Machine Learning. Students will gain hands-on experience with a variety of concepts and tools associated with the field of KDD. Students will also study and apply KDD processes, tools, and techniques, working with small and large data sets representing various business domains and problem types. The class will cover topics such as common database interactions (relational, document, and graph databases), data set creation, data quality analysis, dimension reduction, variance and bias in data, classifier types, and model assessment approaches.

An introduction to building complex software. Students will explore object-oriented (OO) programming and design patterns in depth. We will also focus on developing skills for testing and debugging of software.  Finally, we will utilize a set of frameworks related to software development and testing.

An introduction to different programming language paradigms: functional, logic, and object-oriented programming. Students will also study language concepts such as regular expressions, syntax grammars and semantics. Specific topics may include Perl, Scheme, Java, C++, and Prolog.

An introductory exploration of research in computer science. The students, in collaboration with a faculty mentor, will participate in a research project in a particular area of computer science. The research projects may, for example, include designing new algorithms for computational problems, surveying the research literature, implementing existing algorithms from the research literature, or performing computational experiments.

An introductory exploration of research in computer science. The students, in collaboration with a faculty mentor, will participate in a research project in a particular area of computer science. The research projects may, for example, include designing new algorithms for computational problems, surveying the research literature, implementing existing algorithms from the research literature, or performing computational experiments. 

Topics that complement the established lower level course offerings in computer science will be selected. May be repeated for credit.

Topics that complement the established lower level course offerings in computer science will be selected.   

A study of techniques used to design algorithms for complex computational problems that are efficient in terms of time and memory required during execution. Students will also learn the techniques used to evaluate an algorithm's efficiency. Topics include advanced sorting techniques, advanced data structures, dynamic programming, greedy algorithms, amortized analysis, graph algorithms, network flow algorithms, and linear programming.

Using abstract machine models to measure the complexity of computational problems and to explore the limits of computational power. In addition, students will investigate strategies to deal with intractable computational problems when they arise. Topics include finite automata, Turing machines, undecidability, NP-completeness, approximation algorithms, heuristic algorithms, and experimental algorithmics.

An introduction to the mathematical foundations of machine learning. Students will investigate the mathematics underlying much of machine learning and explore some of the major techniques used in the field. Topics may include gradient descent, perceptrons, support vector machines, kernel methods, backpropagation, and principal components analysis. Three hours of lecture and two hours of lab each week. In the labs, students will apply machine learning techniques to empirical data and quantitatively evaluate and compare the effectiveness of the techniques.

An introduction to multilevel machines, including basic components of a computer, digital circuits, Boolean algebra, microprogramming, machine and assembly languages, and operating systems.

An introduction to the field of artificial intelligence. The course covers the main techniques used to develop computer programs to solve problems that we normally think of as requiring intelligence. Topics include search, games, knowledge representation, logical reasoning systems, and machine learning.

Computer graphics involves using computers to generate images, as opposed to generating images using cameras. Computer graphics images typically try to mimic reality. In this course, students will explore the necessary background for further study in computer graphics. Students will explore the basics of human vision that influence the way computers generate images; projections from three-dimensional space to two-dimensional space; various models of real-world entities such as lighting, surface reflectance, and color; and classic algorithms in computer graphics that students will implement and with which they will have the opportunity to experiment.

An exploration of software engineering and organization principles vital to building large enterprise-scale applications. Leveraging the lessons-learned from readings and using management tools, the class will form a software development team focused on designing, developing, and deploying a multi-tier application. Includes significant reading and programming effort.

An introduction to the fundamental concepts of computer networks. Students will learn how the Internet works. Students will explore real data as it passes through a network; learn how data is interpreted and forwarded from the source to the destination address; implement services to send, transport, and receive data correctly; devise analytical techniques to better understand data collected from real network users; and gather information about known network security attacks.

An introduction to the design, implementation and publishing of mobile applications. Students will learn how to take their innovative ideas from conception to a working prototype mobile application through a series of rigorous hands-on programming assignments and group projects.

An introduction to operating systems: software that safely and fairly shares computer hardware resources among multiple, simultaneously running applications. Topics covered include computer processor (CPU) sharing and scheduling, memory virtualization and management, and file systems. Students will complete programming projects to implement concepts in a real operating system.

Computer Vision (CV) trains machines to understand images and video. Classifying and detecting/recognizing objects in images are common problems that CV tackles. Machine Learning techniques, specifically Convolutional Neural Networks (CNNs), are often used. Students will learn digital image processing techniques that form the basis of many vision algorithms, as well as the concepts behind CNNs for classification.

An introduction to database systems: software that stores, manages, and provides efficient access to a collection of structured information. Topics covered include the relational model, the structured query language (SQL), and algorithms and data structures for efficiently searching and managing data that is larger than main memory. Students will complete a project using a relational database to store and query data related to an application of their choice.

Special study in computing outside of the regular departmental offerings.

Advanced topics that complement the established course offerings in computer science will be selected.

Optional for computer science majors. Recommended for those working toward professional careers or graduate study in computer science, and those seeking to satisfy the criteria for departmental honors.

Optional for computer science majors. Recommended for those working toward professional careers or graduate study in computer science, and those seeking to satisfy the criteria for departmental honors.

Professional experience at an advanced level for juniors and seniors with substantial academic experience in computer science and mathematics. With faculty sponsorship and departmental approval, students may extend their educational experience in computer science, software engineering, or applied mathematics.