Course Information
 2016–2017 Courses:
 Browse by Course Number
 Browse by Term
Fall 2016

CS 111: Introduction to Computer Science
This course will introduce you to computer programming and the design of algorithms. By writing programs to solve problems in areas such as image processing, text processing, and simple games, you will learn about recursive and iterative algorithms, complexity analysis, graphics, data representation, software engineering, and objectoriented design. No previous programming experience is necessary. Students who have received credit for Computer Science 201 or above are not eligible to enroll in Computer Science 111. Students may not simultaneously enroll for CS 108 and CS 111 in the same term. 6 credit; Formal or Statistical Reasoning, Quantitative Reasoning Encounter; offered Fall 2016, Winter 2017, Spring 2017 · S. Goings, A. Rafferty, E. Alexander, L. Oesper, J. Yang, J. Davis 
CS 201: Data Structures
Think back to your favorite assignment from Introduction to Computer Science. Did you ever get the feeling that "there has to be a better/smarter way to do this problem?" The Data Structures course is all about how to store information intelligently and access it efficiently. How can Google take your query, compare it to billions of web pages, and return the answer in less than one second? How can one store information so as to balance the competing needs for fast data retrieval and fast data modification? To help us answer questions like these, we will analyze and implement stacks, queues, trees, linked lists, graphs and hash tables. Students who have received credit for a course for which Computer Science 201 is a prerequisite are not eligible to enroll in Computer Science 201. Prerequisites: Computer Science 111 or instructor permission 6 credit; Formal or Statistical Reasoning; offered Fall 2016, Winter 2017, Spring 2017 · D. Musicant, J. Yang, A. Rafferty 
CS 202: Mathematics of Computer Science
This course introduces some of the formal tools of computer science, using a variety of applications as a vehicle. You'll learn how to encode data so that when you scratch the back of a DVD, it still plays just fine; how to distribute "shares" of your floor's PIN so that any five of you can withdraw money from the floor bank account (but no four of you can); how to play chess; and more. Topics that we'll explore along the way include: logic and proofs, number theory, elementary complexity theory and recurrence relations, basic probability, counting techniques, and graphs. Prerequisites: Computer Science 111 and Mathematics 111 or instructor permission 6 credit; Formal or Statistical Reasoning; offered Fall 2016, Spring 2017 · A. Rafferty, J. Yang 
CS 208: Computer Organization and Architecture
Computer processors are extraordinarily complex systems. The fact that they work at all, let alone as reliably as they do, is a monumental achievement of human collaboration. In this course, we will study the structure of computer processors, with attention to digital logic, assembly language, performance evaluation, computer arithmetic, data paths and control, pipelining, and memory hierarchies. Prerequisites: Computer Science 111 or instructor permission 6 credit; Formal or Statistical Reasoning; offered Fall 2016, Winter 2017 · S. Goings, J. Ondich 
CS 231: Computer Security
Hackers, phishers, and spammersat best they annoy us, at worst they disrupt communication systems, steal identities, bring down corporations, and compromise sensitive systems. In this course, we'll study various aspects of computer and network security, focusing mainly on the technical aspects as well as the social and cultural costs of providing (or not providing) security. Topics include cryptography, authentication and identification schemes, intrusion detection, viruses and worms, spam prevention, firewalls, denial of service, electronic commerce, privacy, and usability. Prerequisites: Computer Science 201 or 202 or 208 6 credit; Formal or Statistical Reasoning; offered Fall 2016 · J. Ondich 
CS 251: Programming Languages: Design and Implementation
What makes a programming language like "Python" or like "Java?" This course will look past superficial properties (like indentation) and into the soul of programming languages. We will explore a variety of topics in programming language construction and design: syntax and semantics, mechanisms for parameter passing, typing, scoping, and control structures. Students will expand their programming experience to include other programming paradigms, including functional languages like Scheme and ML. Prerequisites: Computer Science 201 or instructor permission 6 credit; Formal or Statistical Reasoning; offered Fall 2016, Spring 2017 · D. Musicant, D. LibenNowell 
CS 252: Algorithms
A course on techniques used in the design and analysis of efficient algorithms. We will cover several major algorithmic design paradigms (greedy algorithms, dynamic programming, divide and conquer, and network flow). Along the way, we will explore the application of these techniques to a variety of domains (natural language processing, economics, computational biology, and data mining, for example). As time permits, we will include supplementary topics like randomized algorithms, advanced data structures, and amortized analysis. Prerequisites: Computer Science 201 and either Computer Science 202 or Mathematics 236 6 credit; Formal or Statistical Reasoning; offered Fall 2016, Winter 2017 · L. Oesper 
CS 334: Database Systems
Database systems are used in almost every aspect of computing, from storing data for websites to maintaining financial information for large corporations. Intrinsically, what is a database system and how does it work? This course takes a twopronged approach to studying database systems. From a systems perspective, we will look at the lowlevel details of how a database system works internally, studying such topics as file organization, indexing, sorting techniques, and query optimization. From a theory perspective, we will examine the fundamental ideas behind database systems, such as normal forms and relational algebra. Prerequisites: Computer Science 201 or consent of the instructor. 6 credit; Formal or Statistical Reasoning; offered Fall 2016 · D. Musicant 
CS 352: Advanced Algorithms
A second course on designing and analyzing efficient algorithms to solve computational problems. We will survey some algorithmic design techniques that apply broadly throughout computer science, including discussion of wideranging applications. A sampling of potential topics: approximation algorithms (can we efficiently compute nearoptimal solutions even when finding exact solutions is computationally intractable?); randomized algorithms (does flipping coins help in designing faster/simpler algorithms?); online algorithms (how do we analyze an algorithm that needs to make decisions before the entire input arrives?); advanced data structures; complexity theory. As time and interest permit, we will mix recently published algorithmic papers with classical results. Prerequisites: Computer Science 252 or instructor permission 6 credit; Formal or Statistical Reasoning; offered Fall 2016, Winter 2017 · D. LibenNowell 
CS 399: Senior Seminar
As part of their senior capstone experience, majors will work together in teams (typically four to seven students per team) on facultyspecified topics to design and implement the first stage of a project. Required of all senior majors.
Prerequisites: Senior standing. Students are strongly encouraged to complete Computer Science 252 and either Computer Science 204 or 257 before starting Computer Science 399. 3 credit; Does not fulfill a curricular exploration requirement; offered Fall 2016 · A. Rafferty, D. Musicant, J. Ondich, D. LibenNowell
Winter 2017

CS 111: Introduction to Computer Science
This course will introduce you to computer programming and the design of algorithms. By writing programs to solve problems in areas such as image processing, text processing, and simple games, you will learn about recursive and iterative algorithms, complexity analysis, graphics, data representation, software engineering, and objectoriented design. No previous programming experience is necessary. Students who have received credit for Computer Science 201 or above are not eligible to enroll in Computer Science 111. Students may not simultaneously enroll for CS 108 and CS 111 in the same term. 6 credit; Formal or Statistical Reasoning, Quantitative Reasoning Encounter; offered Fall 2016, Winter 2017, Spring 2017 · S. Goings, A. Rafferty, E. Alexander, L. Oesper, J. Yang, J. Davis 
CS 201: Data Structures
Think back to your favorite assignment from Introduction to Computer Science. Did you ever get the feeling that "there has to be a better/smarter way to do this problem?" The Data Structures course is all about how to store information intelligently and access it efficiently. How can Google take your query, compare it to billions of web pages, and return the answer in less than one second? How can one store information so as to balance the competing needs for fast data retrieval and fast data modification? To help us answer questions like these, we will analyze and implement stacks, queues, trees, linked lists, graphs and hash tables. Students who have received credit for a course for which Computer Science 201 is a prerequisite are not eligible to enroll in Computer Science 201. Prerequisites: Computer Science 111 or instructor permission 6 credit; Formal or Statistical Reasoning; offered Fall 2016, Winter 2017, Spring 2017 · D. Musicant, J. Yang, A. Rafferty 
CS 208: Computer Organization and Architecture
Computer processors are extraordinarily complex systems. The fact that they work at all, let alone as reliably as they do, is a monumental achievement of human collaboration. In this course, we will study the structure of computer processors, with attention to digital logic, assembly language, performance evaluation, computer arithmetic, data paths and control, pipelining, and memory hierarchies. Prerequisites: Computer Science 111 or instructor permission 6 credit; Formal or Statistical Reasoning; offered Fall 2016, Winter 2017 · S. Goings, J. Ondich 
CS 252: Algorithms
A course on techniques used in the design and analysis of efficient algorithms. We will cover several major algorithmic design paradigms (greedy algorithms, dynamic programming, divide and conquer, and network flow). Along the way, we will explore the application of these techniques to a variety of domains (natural language processing, economics, computational biology, and data mining, for example). As time permits, we will include supplementary topics like randomized algorithms, advanced data structures, and amortized analysis. Prerequisites: Computer Science 201 and either Computer Science 202 or Mathematics 236 6 credit; Formal or Statistical Reasoning; offered Fall 2016, Winter 2017 · L. Oesper 
CS 254: Computability and Complexity
An introduction to the theory of computation. What problems can and cannot be solved efficiently by computers? What problems cannot be solved by computers, period? Topics include formal models of computation, including finitestate automata, pushdown automata, and Turing machines; formal languages, including regular expressions and contextfree grammars; computability and uncomputability; and computational complexity, particularly NPcompleteness. Prerequisites: Computer Science 111 and either Computer Science 202 or Mathematics 236 6 credit; Formal or Statistical Reasoning; offered Winter 2017, Spring 2017 · J. Yang, D. LibenNowell 
CS 257: Software Design
It's easy to write a mediocre computer program, and lots of people do it. Good programs are quite a bit harder to write, and are correspondingly less common. In this course, we will study techniques, tools, and habits that will improve your chances of writing good software. While working on several mediumsized programming projects, we will investigate code construction techniques, debugging and profiling tools, testing methodologies, UML, principles of objectoriented design, design patterns, and user interface design. Prerequisites: Computer Science 201 or instructor permission 6 credit; Formal or Statistical Reasoning; offered Winter 2017, Spring 2017 · J. Ondich, E. Alexander 
CS 311: Computer Graphics
Scientific simulations, movies, and video games often incorporate computergenerated images of fictitious worlds. How are these worlds modeled inside a computer? How are they "photographed" to produce the images that we see? What performance constraints and design tradeoffs come into play? In this course we learn the basic theory and methodology of computer graphics, following the historical development of the field, from software implementations to fixedfunction hardware, shader programs, and recent lowerlevel interfaces. Collaborative final projects allow students to pursue special topics in greater depth. Familiarity with vectors, matrices, and the C programming language is recommended but not required.
Prerequisites: Computer Science 201 6 credit; Quantitative Reasoning Encounter, Formal or Statistical Reasoning; offered Winter 2017 · J. Davis 
CS 321: Artificial Intelligence
How can we design computer systems with behavior that seems "intelligent?" This course will examine a number of different approaches to this question, including intelligent search computer game playing, automated logic, machine learning (including neural networks), and reasoning with uncertainty. The coursework is a mix of problem solving and computer programming based on the ideas that we discuss. Prerequisites: Computer Science 201, additionally Computer Science 202 or Mathematics 236 are strongly recommended. 6 credit; Formal or Statistical Reasoning; offered Winter 2017 · A. Rafferty 
CS 341: Cryptography
Modern cryptographic systems allow parties to communicate in a secure way, even if they don't trust the channels over which they are communicating (or maybe even each other). Cryptography is at the heart of a huge range of applications: online banking and shopping, passwordprotected computer accounts, and secure wireless networks, to name just a few. In this course, we will introduce and explore some fundamental cryptographic primitives, using a rigorous, proofbased approach. Topics will include publickey encryption, digital signatures, pseudorandom number generation, zero knowledge, and novel applications of cryptography.
Prerequisites: Computer Science 201 plus either Computer Science 202 or Mathematics 236. One or more of Computer Science 252, Computer Science 254 and Mathematics 265 are also recommended 6 credit; Formal or Statistical Reasoning, Quantitative Reasoning Encounter; offered Winter 2017 · D. LibenNowell 
CS 352: Advanced Algorithms
A second course on designing and analyzing efficient algorithms to solve computational problems. We will survey some algorithmic design techniques that apply broadly throughout computer science, including discussion of wideranging applications. A sampling of potential topics: approximation algorithms (can we efficiently compute nearoptimal solutions even when finding exact solutions is computationally intractable?); randomized algorithms (does flipping coins help in designing faster/simpler algorithms?); online algorithms (how do we analyze an algorithm that needs to make decisions before the entire input arrives?); advanced data structures; complexity theory. As time and interest permit, we will mix recently published algorithmic papers with classical results. Prerequisites: Computer Science 252 or instructor permission 6 credit; Formal or Statistical Reasoning; offered Fall 2016, Winter 2017 · D. LibenNowell 
CS 400: Integrative Exercise
Beginning with the prototypes developed in the Senior Seminar, project teams will complete their project and present it to the department. Required of all senior majors. Prerequisites: Computer Science 399 3 credit; S/NC; offered Winter 2017 · A. Rafferty, D. Musicant, J. Ondich, D. LibenNowell
Spring 2017

CS 111: Introduction to Computer Science
This course will introduce you to computer programming and the design of algorithms. By writing programs to solve problems in areas such as image processing, text processing, and simple games, you will learn about recursive and iterative algorithms, complexity analysis, graphics, data representation, software engineering, and objectoriented design. No previous programming experience is necessary. Students who have received credit for Computer Science 201 or above are not eligible to enroll in Computer Science 111. Students may not simultaneously enroll for CS 108 and CS 111 in the same term. 6 credit; Formal or Statistical Reasoning, Quantitative Reasoning Encounter; offered Fall 2016, Winter 2017, Spring 2017 · S. Goings, A. Rafferty, E. Alexander, L. Oesper, J. Yang, J. Davis 
CS 201: Data Structures
Think back to your favorite assignment from Introduction to Computer Science. Did you ever get the feeling that "there has to be a better/smarter way to do this problem?" The Data Structures course is all about how to store information intelligently and access it efficiently. How can Google take your query, compare it to billions of web pages, and return the answer in less than one second? How can one store information so as to balance the competing needs for fast data retrieval and fast data modification? To help us answer questions like these, we will analyze and implement stacks, queues, trees, linked lists, graphs and hash tables. Students who have received credit for a course for which Computer Science 201 is a prerequisite are not eligible to enroll in Computer Science 201. Prerequisites: Computer Science 111 or instructor permission 6 credit; Formal or Statistical Reasoning; offered Fall 2016, Winter 2017, Spring 2017 · D. Musicant, J. Yang, A. Rafferty 
CS 202: Mathematics of Computer Science
This course introduces some of the formal tools of computer science, using a variety of applications as a vehicle. You'll learn how to encode data so that when you scratch the back of a DVD, it still plays just fine; how to distribute "shares" of your floor's PIN so that any five of you can withdraw money from the floor bank account (but no four of you can); how to play chess; and more. Topics that we'll explore along the way include: logic and proofs, number theory, elementary complexity theory and recurrence relations, basic probability, counting techniques, and graphs. Prerequisites: Computer Science 111 and Mathematics 111 or instructor permission 6 credit; Formal or Statistical Reasoning; offered Fall 2016, Spring 2017 · A. Rafferty, J. Yang 
CS 251: Programming Languages: Design and Implementation
What makes a programming language like "Python" or like "Java?" This course will look past superficial properties (like indentation) and into the soul of programming languages. We will explore a variety of topics in programming language construction and design: syntax and semantics, mechanisms for parameter passing, typing, scoping, and control structures. Students will expand their programming experience to include other programming paradigms, including functional languages like Scheme and ML. Prerequisites: Computer Science 201 or instructor permission 6 credit; Formal or Statistical Reasoning; offered Fall 2016, Spring 2017 · D. Musicant, D. LibenNowell 
CS 254: Computability and Complexity
An introduction to the theory of computation. What problems can and cannot be solved efficiently by computers? What problems cannot be solved by computers, period? Topics include formal models of computation, including finitestate automata, pushdown automata, and Turing machines; formal languages, including regular expressions and contextfree grammars; computability and uncomputability; and computational complexity, particularly NPcompleteness. Prerequisites: Computer Science 111 and either Computer Science 202 or Mathematics 236 6 credit; Formal or Statistical Reasoning; offered Winter 2017, Spring 2017 · J. Yang, D. LibenNowell 
CS 257: Software Design
It's easy to write a mediocre computer program, and lots of people do it. Good programs are quite a bit harder to write, and are correspondingly less common. In this course, we will study techniques, tools, and habits that will improve your chances of writing good software. While working on several mediumsized programming projects, we will investigate code construction techniques, debugging and profiling tools, testing methodologies, UML, principles of objectoriented design, design patterns, and user interface design. Prerequisites: Computer Science 201 or instructor permission 6 credit; Formal or Statistical Reasoning; offered Winter 2017, Spring 2017 · J. Ondich, E. Alexander 
CS 314: Data Visualization
Understanding the wealth of data that surrounds us can be challenging. Luckily, we have evolved incredible tools for finding patterns in large amounts of information: our eyes! Data visualization is concerned with taking information and turning it into pictures to better communicate patterns or discover new insights. It combines aspects of computer graphics, humancomputer interaction, design, and perceptual psychology. In this course, we will learn the different ways in which data can be expressed visually and which methods work best for which tasks. Using this knowledge, we will critique existing visualizations as well as design and build new ones.
Prerequisites: Computer Science 201 6 credit; Formal or Statistical Reasoning, Quantitative Reasoning Encounter; offered Spring 2017 · E. Alexander 
CS 324: Data Mining
How does Google always understand what it is you're looking for? How does Amazon.com figure out what items you might be interested in buying? How can categories of similar politicians be identified, based on their voting patterns? These questions can be answered via data mining, a field of study at the crossroads of artificial intelligence, database systems, and statistics. Data mining concerns itself with the goal of getting a computer to learn or discover patterns, especially those found within large datasets. We'll focus on techniques such as classification, clustering, association rules, web mining, collaborative filtering, and others. Prerequisites: Computer Science 201, additionally, Computer Science 202 or Mathematics 236 strongly recommended 6 credit; Formal or Statistical Reasoning, Quantitative Reasoning Encounter; offered Spring 2017 · L. Oesper 
CS 361: Evolutionary Computing and Artificial Life
An introduction to evolutionary computation and artificial life, with a special emphasis on the two way flow of ideas between evolutionary biology and computer science. Topics will include the basic principles of biological evolution, experimental evolution techniques, and the application of evolutionary computation principles to solve real problems. All students will be expected to complete and present a term project exploring an open question in evolutionary computation. Prerequisites: Computer Science 201 6 credit; Formal or Statistical Reasoning; offered Spring 2017 · S. GoingsExtended departmental description for CS 361
Have you ever wished that instead of spending 2 hours writing a program to solve a difficult problem you could instead just tell the computer to do the work and go play Ultimate Frisbee for 2 hours knowing the solution will be waiting for you when you return? One of the goals of artificial intelligence is to be able to view the computer as a "black box", you simply give it the problem you want to solve, and it gives you the answer, without you needed to understand all of the internal workings. Evolutionary computation seeks to create this black box by harnessing the power of Darwinian evolution to solve computational problems. Instead of programming a solution, the user simply initializes a population of very simple (and probably very bad) solutions, and then sits back while the population evolves until a good solution appears. Evolutionary Computation (EC) has shown promise in evolving novel solutions to realworld problems, such as antennas actually deployed on Nasa satellites, neural controllers for legged robots, and programs that choose sound investments, however EC is a current active field of research with many open questions to be answered. In this course students will develop a broad understanding of developing and analyzing current evolutionary computation systems, and develop a deeper understanding of at least one specific evolutionary computation topic through a research project.