Computer Science Classes

This course provides an introduction to the handling, analysis, and interpretation of the big 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 natural and social sciences, statistics, and mathematics. The course is intended to be accessible to all students, regardless of background.

General Education Requirements:
Quantitative Thinking Q2

Distribution Requirements:
Natural science and mathematics

Course Materials

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 examples from a range of application areas including multimedia processing, turtle graphics, and text processing. Course work will use the Python programming language.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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 examples from a range of application areas including multimedia processing, turtle graphics, and text processing. Course work will use the Python programming language.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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 examples from a range of application areas including multimedia processing, turtle graphics, and text processing. Course work will use the Python programming language.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

What happens as software grows in complexity? How do we break a program into manageable pieces? How do we write readable, maintainable code? This course is an introduction to the building blocks of software design: abstraction, decomposition, and encapsulation. Using object-oriented programming in Java, we will create graphics, games, and simulations, and explore natural language processing. Topics may include: classes, objects, polymorphism, inheritance, testing, refactoring, events, closures, streams, immutability, parallel programming, and version control. The course culminates in a student-designed project. There is a required 1.5 hour laboratory section associated with this course. Prerequisite(s): COMP 123 or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

What happens as software grows in complexity? How do we break a program into manageable pieces? How do we write readable, maintainable code? This course is an introduction to the building blocks of software design: abstraction, decomposition, and encapsulation. Using object-oriented programming in Java, we will create graphics, games, and simulations, and explore natural language processing. Topics may include: classes, objects, polymorphism, inheritance, testing, refactoring, events, closures, streams, immutability, parallel programming, and version control. The course culminates in a student-designed project. There is a required 1.5 hour laboratory section associated with this course. Prerequisite(s): COMP 123 or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

This course familiarizes students with the fundamental data structures in computer science. Using the Java programming language, students will study existing data structure implementations, implement their own data structures, and develop data-intensive applications. The course covers stacks, queues, lists, trees, heaps, hash tables, graphs, and the common algorithms that use these data structures. Students will also receive an introduction to basic complexity analysis (Big-O), learn the time complexity of different data structure operations, and gain experience in calculating the time complexity of programs that use data structures. Prerequisite(s): COMP 127 or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

This course looks at ethical questions connected with the internet as we know it today: an online environment where content is generated and shared through user activities such as blogging, media sharing, social networking, tagging, tweeting, virtual world gaming, wiki developing, and the like. We will start by considering debates over freedom of speech, privacy, surveillance, and intellectual property: issues that pre-exist the development of the Internet, but which because of it have taken on new dimensions. From here we will go on to take up some ethical questions arising from four different domains of activity on the social web: gaming, social networking, blog/wiki developing, and "hacktivism." In the third part of the course, we will consider broad questions connected to the integration of the Internet with devices other than the personal computer and mobile phone and which open the prospect of a world of integrated networked systems. What are some of the impacts of such integration on our everyday ethical relations with others and on the overall quality of our lives? How does being networked affect the meaning of being human?

General Education Requirements:

Distribution Requirements:
Humanities

Course Materials

Have you ever played a game using the Nintendo Wii remote, Xbox Kinect, or Sony Move? Have you used augmented reality mobile apps like Pokemon Go or ones that identify stars and planets that you point the device towards. What about tilting a mobile device to change from portrait to landscape mode? All of these examples represent 3D user interfaces that allow a user to interact with computers in 3D space using physical gestures or tracked controllers. This course introduces how to interact with virtual worlds using virtual reality displays and 3D user interfaces, including design metaphors, tracking technology, interfaces for selecting and manipulating 3D objects, traveling and wayfinding to get from one real or virtual place to another, and system control and symbolic input such as menus and text. Along the way, you will study the design and implementation of algorithms and programs and how to represent information with a computer. This course serves students who might envision focusing on computer science in college and students who wish to have solid computing skills to support work in another field. Only basic high school math (through algebra and trigonometry) is required as a prerequisite. Nonetheless, even if you have extensive experience with computer science or programming (including advanced placement or similar) you may find new and challenging material in this course because of its emphasis on applications, user interaction, and virtual reality. COMP 194 will serve as a replacement for the COMP 123 prerequisite if you register for COMP 127 in the future.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

This course offers an in-depth introduction to the design and analysis of algorithms. Students will work with algorithms in pseudocode, and will learn formal and informal methods for analyzing algorithm efficiency and correctness. Topics may include recursion, divide and conquer, dynamic programming, greedy methods, branch and bound, randomized, probabilistic, and parallel algorithms. Application areas include string processing, graphs, geometric problems, and optimization. This course will introduce computability topics including regular expressions, grammars and parsing, automata, nondeterminism, and NP completeness. Prerequisite(s): COMP 128 (or COMP 124, if previously taken) and MATH 279, or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

This course is an introduction to the problem of building software with humans and for humans. Students work in teams to design and implement a semester-long user-facing software project of their own invention. There are no limitations on topic or technology; on the contrary, students are responsible for imagining possibilities, articulating goals, and researching and selecting suitable technologies. The format resembles a studio art class, with in-class discussion guided by sharing and critiquing classmates' ongoing work. Topics include communication, division of labor, user-centered design, human-computer interaction, product management, project management, iterative development, engineering tradeoffs, separation of concerns, code readability and maintainability, refactoring, testing, and version control. Teams give a public demonstration of their working projects at the end of the semester. Prerequisite(s): COMP 127 (COMP 128 recommended), or COMP 124 if previously taken, or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

This course is an introduction to the problem of building software with humans and for humans. Students work in teams to design and implement a semester-long user-facing software project of their own invention. There are no limitations on topic or technology; on the contrary, students are responsible for imagining possibilities, articulating goals, and researching and selecting suitable technologies. The format resembles a studio art class, with in-class discussion guided by sharing and critiquing classmates' ongoing work. Topics include communication, division of labor, user-centered design, human-computer interaction, product management, project management, iterative development, engineering tradeoffs, separation of concerns, code readability and maintainability, refactoring, testing, and version control. Teams give a public demonstration of their working projects at the end of the semester. Prerequisite(s): COMP 127 (COMP 128 recommended), or COMP 124 if previously taken, or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

This course familiarizes the student with the internal design and organization of computers. Topics include number systems, internal data representations, microarchitectures, the functional units of a computer system, memory, processor, and input/output structures, instruction sets and assembly language, addressing techniques, system software, and concurrency and parallelism. Prerequisite(s): COMP 127 (or COMP 124 if previously taken) or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

This interdisciplnary course will examine selected topics in computational biology, including basic bioinformatics, algorithms used in genomics and genome analysis, computational techniques for systems biology, and synthetic biology. Prerequisite(s): COMP 123; BIOL 190 recommended

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

The modern Information Age has produced a wealth of data about the complex networks that tie us together. In response, the field of Network Science has arisen, bringing together mathematics, computer science, sociology, biology, economics and other fields. This course will explore the fundamental questions and the mathematical tools of Network Science. This includes: the structure of complex networks, including connectedness, centrality and "long tails"; community detection; random/strategic models for network formation; diffusion/contagion and "tipping points" on networks; and algorithms for analyzing complex networks. Prerequisite(s): COMP 123, MATH 236 and MATH 379

General Education Requirements:
Writing WP
Quantitative Thinking Q2

Distribution Requirements:
Natural science and mathematics

Course Materials

An introduction to computer security and privacy. Topics will include privacy, threat modeling, software security, web tracking, web security, usable privacy and security, authentication, anonymity, network security, social engineering, the relationship of the law to security and privacy, and ethics. This course will include hands-on experience with security exploits in a Linux environment and student-led discussions of research papers. Students will complete a capstone project in a security- or privacy-related topic of their choice. Prerequisite: COMP 240.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

This course provides an introduction to the handling, analysis, and interpretation of the big 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 natural and social sciences, statistics, and mathematics. The course is intended to be accessible to all students, regardless of background.

General Education Requirements:
Quantitative Thinking Q2

Distribution Requirements:
Natural science and mathematics

Course Materials

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 examples from a range of application areas including multimedia processing, turtle graphics, and text processing. Course work will use the Python programming language.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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 examples from a range of application areas including multimedia processing, turtle graphics, and text processing. Course work will use the Python programming language.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

What happens as software grows in complexity? How do we break a program into manageable pieces? How do we write readable, maintainable code? This course is an introduction to the building blocks of software design: abstraction, decomposition, and encapsulation. Using object-oriented programming in Java, we will create graphics, games, and simulations, and explore natural language processing. Topics may include: classes, objects, polymorphism, inheritance, testing, refactoring, events, closures, streams, immutability, parallel programming, and version control. The course culminates in a student-designed project. There is a required 1.5 hour laboratory section associated with this course. Prerequisite(s): COMP 123 or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

What happens as software grows in complexity? How do we break a program into manageable pieces? How do we write readable, maintainable code? This course is an introduction to the building blocks of software design: abstraction, decomposition, and encapsulation. Using object-oriented programming in Java, we will create graphics, games, and simulations, and explore natural language processing. Topics may include: classes, objects, polymorphism, inheritance, testing, refactoring, events, closures, streams, immutability, parallel programming, and version control. The course culminates in a student-designed project. There is a required 1.5 hour laboratory section associated with this course. Prerequisite(s): COMP 123 or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

This course familiarizes students with the fundamental data structures in computer science. Using the Java programming language, students will study existing data structure implementations, implement their own data structures, and develop data-intensive applications. The course covers stacks, queues, lists, trees, heaps, hash tables, graphs, and the common algorithms that use these data structures. Students will also receive an introduction to basic complexity analysis (Big-O), learn the time complexity of different data structure operations, and gain experience in calculating the time complexity of programs that use data structures. Prerequisite(s): COMP 127 or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

This course is an introduction to the problem of building software with humans and for humans. Students work in teams to design and implement a semester-long user-facing software project of their own invention. There are no limitations on topic or technology; on the contrary, students are responsible for imagining possibilities, articulating goals, and researching and selecting suitable technologies. The format resembles a studio art class, with in-class discussion guided by sharing and critiquing classmates' ongoing work. Topics include communication, division of labor, user-centered design, human-computer interaction, product management, project management, iterative development, engineering tradeoffs, separation of concerns, code readability and maintainability, refactoring, testing, and version control. Teams give a public demonstration of their working projects at the end of the semester. Prerequisite(s): COMP 127 (COMP 128 recommended), or COMP 124 if previously taken, or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

This course familiarizes the student with the internal design and organization of computers. Topics include number systems, internal data representations, microarchitectures, the functional units of a computer system, memory, processor, and input/output structures, instruction sets and assembly language, addressing techniques, system software, and concurrency and parallelism. Prerequisite(s): COMP 127 (or COMP 124 if previously taken) or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

Virtual reality (VR) for gaming and entertainment has exploded in popular culture. VR engages the senses (visual, auditory, and haptic) in revolutionary ways, allowing users to immerse themselves in new worlds, to become rather than play a character. As a result, VR has quickly emerged at the front edge of experimentation in storytelling and narrative design. It has also become one of the most creative popular-cultural sites for experimenting with understanding other minds and identities. Cross-listed between Computer Science and English, this course is an introduction to the challenge of building immersive virtual reality (VR) games through the lens of narrative and storytelling. Students will work in interdisciplinary teams to bring world-building narrative techniques to an immersive visual setting while exploring technical challenges involved in programming and game development through hands-on projects. Permission of the instructor is required to register. Fill out the interest survey to request permission: https://forms.gle/kFHKzy518zAhJZzHAPrerequisite(s) if registering for COMP 394: COMP 124 or COMP 127 is required. COMP 225 is recommended.

General Education Requirements:

Distribution Requirements:

Course Materials

This remote class will explore various methods of, and approaches to, software testing. You will work in small groups based around time zones and schedules, and participate in discussions with the whole class through an online asynchronous discussion platform. Some reading and discussion of software failures in real life will also occur. Be prepared to share a large-ish program that you have written (ideally in Java or Python), either a standalone project or a homework from COMP 124/COMP 127. Prerequisites: COMP 124 or COMP 127.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

This course introduces the theory and practice of data science applied to online communities such as Wikipedia, Facebook, and Twitter. Students will read and discuss recent academic research papers that analyze behavior on these websites and use computational simulation, machine learning, and data-mining techniques to analyze massive behavioral datasets in areas such as recommender systems, natural language processing, and tagging systems. This course counts as the capstone. Prerequisite(s): COMP 221 or permission of instructor.

General Education Requirements:
Quantitative Thinking Q3

Distribution Requirements:
Natural science and mathematics

Course Materials

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, and natural language processing. This course involves programming in Python; students should have a basic familiarity with Python or be prepared to learn Python during the course. This course counts as the capstone. Prerequisite(s): COMP 221, or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

This course provides an introduction to the handling, analysis, and interpretation of the big 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 natural and social sciences, statistics, and mathematics. The course is intended to be accessible to all students, regardless of background.

General Education Requirements:
Quantitative Thinking Q2

Distribution Requirements:
Natural science and mathematics

Course Materials

This course provides an introduction to the handling, analysis, and interpretation of the big 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 natural and social sciences, statistics, and mathematics. The course is intended to be accessible to all students, regardless of background.

General Education Requirements:
Quantitative Thinking Q2

Distribution Requirements:
Natural science and mathematics

Course Materials

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 examples from a range of application areas including multimedia processing, turtle graphics, and text processing. Course work will use the Python programming language.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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 examples from a range of application areas including multimedia processing, turtle graphics, and text processing. Course work will use the Python programming language.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

What happens as software grows in complexity? How do we break a program into manageable pieces? How do we write readable, maintainable code? This course is an introduction to the building blocks of software design: abstraction, decomposition, and encapsulation. Using object-oriented programming in Java, we will create graphics, games, and simulations, and explore natural language processing. Topics may include: classes, objects, polymorphism, inheritance, testing, refactoring, events, closures, streams, immutability, parallel programming, and version control. The course culminates in a student-designed project. There is a required 1.5 hour laboratory section associated with this course. Prerequisite(s): COMP 123 or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

What happens as software grows in complexity? How do we break a program into manageable pieces? How do we write readable, maintainable code? This course is an introduction to the building blocks of software design: abstraction, decomposition, and encapsulation. Using object-oriented programming in Java, we will create graphics, games, and simulations, and explore natural language processing. Topics may include: classes, objects, polymorphism, inheritance, testing, refactoring, events, closures, streams, immutability, parallel programming, and version control. The course culminates in a student-designed project. There is a required 1.5 hour laboratory section associated with this course. Prerequisite(s): COMP 123 or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

What are the truths and falsehoods that data and databases tell us about contemporary culture? How do we use data to tell stories? Increasingly, computational methods are being used to ask questions and look for patterns in cultural data (from museums, libraries, archives, and elsewhere). This class provides an introduction to some of the methods and tools used to investigate data and datasets, while encouraging critical engagement with a number of humanities-based research questions surrounding them. Students will consider ethical and design questions encompassing the collection, analysis, and presentation of data through class conversations and the process of creating their own digital projects.

General Education Requirements:
Quantitative Thinking Q2

Distribution Requirements:
Humanities

Course Materials

This course offers an in-depth introduction to the design and analysis of algorithms. Students will work with algorithms in pseudocode, and will learn formal and informal methods for analyzing algorithm efficiency and correctness. Topics may include recursion, divide and conquer, dynamic programming, greedy methods, branch and bound, randomized, probabilistic, and parallel algorithms. Application areas include string processing, graphs, geometric problems, and optimization. This course will introduce computability topics including regular expressions, grammars and parsing, automata, nondeterminism, and NP completeness. Prerequisite(s): COMP 128 (or COMP 124, if previously taken) and MATH 279, or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

This course is an introduction to the problem of building software with humans and for humans. Students work in teams to design and implement a semester-long user-facing software project of their own invention. There are no limitations on topic or technology; on the contrary, students are responsible for imagining possibilities, articulating goals, and researching and selecting suitable technologies. The format resembles a studio art class, with in-class discussion guided by sharing and critiquing classmates' ongoing work. Topics include communication, division of labor, user-centered design, human-computer interaction, product management, project management, iterative development, engineering tradeoffs, separation of concerns, code readability and maintainability, refactoring, testing, and version control. Teams give a public demonstration of their working projects at the end of the semester. Prerequisite(s): COMP 127 (COMP 128 recommended), or COMP 124 if previously taken, or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

This course familiarizes the student with the internal design and organization of computers. Topics include number systems, internal data representations, microarchitectures, the functional units of a computer system, memory, processor, and input/output structures, instruction sets and assembly language, addressing techniques, system software, and concurrency and parallelism. Prerequisite(s): COMP 127 (or COMP 124 if previously taken) or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

This course will investigate the theory and practice of computer graphics programming using C++ and OpenGL. Through hands-on projects, supported by lecture and discussion, you will learn the fundamentals of creating interactive 2D and 3D images with applications in art, design, games, movies, science, and medicine. Topics covered will include event loops, polygonal models, rendering techniques, texturing, lighting, interaction techniques, and virtual reality. This course counts as the capstone. Prerequisite(s): COMP 240; Linear Algebra recommended but not required.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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 examples from a range of application areas including multimedia processing, turtle graphics, and text processing. Course work will use the Python programming language.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

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 examples from a range of application areas including multimedia processing, turtle graphics, and text processing. Course work will use the Python programming language.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

What happens as software grows in complexity? How do we break a program into manageable pieces? How do we write readable, maintainable code? This course is an introduction to the building blocks of software design: abstraction, decomposition, and encapsulation. Using object-oriented programming in Java, we will create graphics, games, and simulations, and explore natural language processing. Topics may include: classes, objects, polymorphism, inheritance, testing, refactoring, events, closures, streams, immutability, parallel programming, and version control. The course culminates in a student-designed project. There is a required 1.5 hour laboratory section associated with this course. Prerequisite(s): COMP 123 or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

This course familiarizes students with the fundamental data structures in computer science. Using the Java programming language, students will study existing data structure implementations, implement their own data structures, and develop data-intensive applications. The course covers stacks, queues, lists, trees, heaps, hash tables, graphs, and the common algorithms that use these data structures. Students will also receive an introduction to basic complexity analysis (Big-O), learn the time complexity of different data structure operations, and gain experience in calculating the time complexity of programs that use data structures. Prerequisite(s): COMP 127 or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

This course familiarizes students with the fundamental data structures in computer science. Using the Java programming language, students will study existing data structure implementations, implement their own data structures, and develop data-intensive applications. The course covers stacks, queues, lists, trees, heaps, hash tables, graphs, and the common algorithms that use these data structures. Students will also receive an introduction to basic complexity analysis (Big-O), learn the time complexity of different data structure operations, and gain experience in calculating the time complexity of programs that use data structures. Prerequisite(s): COMP 127 or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

This course offers an in-depth introduction to the design and analysis of algorithms. Students will work with algorithms in pseudocode, and will learn formal and informal methods for analyzing algorithm efficiency and correctness. Topics may include recursion, divide and conquer, dynamic programming, greedy methods, branch and bound, randomized, probabilistic, and parallel algorithms. Application areas include string processing, graphs, geometric problems, and optimization. This course will introduce computability topics including regular expressions, grammars and parsing, automata, nondeterminism, and NP completeness. Prerequisite(s): COMP 128 (or COMP 124, if previously taken) and MATH 279, or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

This course is an introduction to the problem of building software with humans and for humans. Students work in teams to design and implement a semester-long user-facing software project of their own invention. There are no limitations on topic or technology; on the contrary, students are responsible for imagining possibilities, articulating goals, and researching and selecting suitable technologies. The format resembles a studio art class, with in-class discussion guided by sharing and critiquing classmates' ongoing work. Topics include communication, division of labor, user-centered design, human-computer interaction, product management, project management, iterative development, engineering tradeoffs, separation of concerns, code readability and maintainability, refactoring, testing, and version control. Teams give a public demonstration of their working projects at the end of the semester. Prerequisite(s): COMP 127 (COMP 128 recommended), or COMP 124 if previously taken, or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

This course familiarizes the student with the internal design and organization of computers. Topics include number systems, internal data representations, microarchitectures, the functional units of a computer system, memory, processor, and input/output structures, instruction sets and assembly language, addressing techniques, system software, and concurrency and parallelism. Prerequisite(s): COMP 127 (or COMP 124 if previously taken) or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

This course examines the theoretical foundations of computation. It explores different mathematical models that try to formalize our informal notion of an algorithm. Models include finite automata, regular expressions, grammars, and Turing machines. The course also discusses ideas about what can and cannot be computed. In addition, the course explores the basics of complexity theory, examining broad categories of problems and their algorithms, and their efficiency. The focus is on the question of P versus NP, and the NP-complete set. Prerequisite(s): (COMP 128 or COMP 221 or COMP 124 if previously taken) and MATH 279, or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

A mix of applied linear algebra and numerical analysis, 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, such as subspaces, projections, and matrix decompositions. The course reviews these concepts, adopts them to large scales, and applies them in the core techniques of scientific computing. These include solving systems of linear and nonlinear equations, approximation and statistical function estimation, optimization, interpolation, eigenvalue and singular value decompositions, and compression. Applications throughout the natural sciences, social sciences, statistics, and computer science. Prerequisite(s): COMP 120 or COMP 123, and MATH 236

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

A mix of applied linear algebra and numerical analysis, 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, such as subspaces, projections, and matrix decompositions. The course reviews these concepts, adopts them to large scales, and applies them in the core techniques of scientific computing. These include solving systems of linear and nonlinear equations, approximation and statistical function estimation, optimization, interpolation, eigenvalue and singular value decompositions, and compression. Applications throughout the natural sciences, social sciences, statistics, and computer science. Prerequisite(s): COMP 120 or COMP 123, and MATH 236

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials

This course provides an introduction to reinforcement learning, which is a machine learning technique. Reinforcement learning is concerned with building programs that learn how to take actions in an environment with a goal of maximizing "rewards". It has applications in robotics, computer games, marketing, health, and finance, among others. We will study theoretical properties and practical applications of reinforcement learning. Students will demonstrate their learning through a project. This course involves programming in Python. Prerequisite(s): COMP221 or permission of instructor.

General Education Requirements:

Distribution Requirements:

Course Materials

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. This course counts as the capstone. Prerequisite(s): COMP 240 and COMP 221, or permission of instructor.

General Education Requirements:

Distribution Requirements:
Natural science and mathematics

Course Materials