Course Information
 2015–2016 Courses:
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CS 099: Summer Computer Science Institute
Computer science is a rich academic field that seeks to systematically study the processes for solving problems and untangle the complexities in the concrete physical world and the abstract mathematical world. The Summer Computer Science Institute (SCSI) at Carleton focuses on understanding how to think about these processes, how to program computers to implement them, and how to apply computer science ideas to real problems of interest. Students at SCSI will learn how to systematically approach problems like a computer scientist as they engage in classroom learning, handson lab activities, and collaborative guided research. 6 credit; S/CR/NC; Does not fulfill a curricular exploration requirement; not offered 2015–2016 · D. Musicant 
CS 108: Life in the Age of Networks
This course investigates how the social, technological, and natural worlds are connected, and how the study of networks sheds light on these connections. A network is a collection of entities linked by some relationship: people connected by friendships (e.g., Facebook); web pages connected by hyperlinks; species connected by the whopreysonwhom relationship. We will explore mathematical properties of networks while emphasizing the efficient processing and analysis of network data drawn from a variety of fields. Topics include: how Google works; "six degrees of separation"; the spread of fads through society. No background in computer science or programming is required or expected. Prerequisites: Students may not simultaneously enroll in Computer Science 108 and Computer Science 111 in the same term, and students who have received credit for Computer Science 111 or above are not eligible to enroll in Computer Science 108 6 credit; Formal or Statistical Reasoning, Quantitative Reasoning Encounter; not offered 2015–2016 
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 2015, Winter 2016, Spring 2016 · S. Goings, L. Oesper, L. Milne, A. Rafferty, A. Exley 
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, Quantitative Reasoning Encounter; offered Fall 2015, Winter 2016, Spring 2016 · A. Exley, S. Goings, A. Csizmar Dalal 
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 2015, Winter 2016, Spring 2016 · L. Oesper 
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 2015, Winter 2016 · A. Exley, S. Goings 
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; not offered 2015–2016 
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 2015, Spring 2016 · D. Musicant 
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 2015, Winter 2016 · L. Milne, J. Ondich 
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 Fall 2015, Spring 2016 · A. Rafferty 
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 2016, Spring 2016 · J. Ondich, A. Csizmar Dalal 
CS 312: Audio Programming
Students will learn the basics of MIDI and Digital Audio programming using C++. In the MIDI portion of the course, you'll learn to record, play, and transform MIDI data. You'll learn to read, write, and play standard MIDI files. During the Digital Audio portion of the course, you'll learn the basics of audio synthesis: oscillators, envelopes, filters, amplifiers, and FFT analyses. Weekly homework assignments and two major group projects. Prerequisites: Computer Science 201 or instructor permission 6 credit; Formal or Statistical Reasoning; offered Winter 2016 · J. Ellinger 
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; not offered 2015–2016 
CS 322: Natural Language Processing
Computers are poor conversationalists, despite decades of attempts to change that fact. This course will provide an overview of the computational techniques developed in the attempt to enable computers to interpret and respond appropriately to ideas expressed using natural languages (such as English or French) as opposed to formal languages (such as C++ or Lisp). Topics in this course will include parsing, semantic analysis, machine translation, dialogue systems, and statistical methods in speech recognition. Prerequisites: Computer Science 201 and either Computer Science 202 or Mathematics 236, or instructor permission 6 credit; Formal or Statistical Reasoning; offered Fall 2015 · A. Exley 
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; not offered 2015–2016 
CS 328: Computational Models of Cognition
How are machine learning and human learning similar? What sorts of things can people learn, and how can we apply computer science ideas to characterize cognition? This interdisciplinary course will take a computational modeling approach, exploring how models can help us to better understand cognition and observing similarities between machine learning methods and cognitive tasks. Through in class activities and readings of both classic and contemporary research papers on computational cognitive modeling, we'll build up an understanding of how different modeling choices lead to different predictions about human behavior and investigate potential practical uses of cognitive models. Final collaborative research projects will allow you to apply your modeling skills to a cognitive phenomenon that you're interested in. Prerequisites: Computer Science 201 or instructor permission. Computer Science 202 strongly recommended 6 credit; Formal or Statistical Reasoning, Quantitative Reasoning Encounter; offered Spring 2016 · A. Rafferty 
CS 331: Computer Networks
The Internet is composed of a large number of heterogeneous, independentlyoperating computer networks that work together to transport all sorts of data to points all over the world. The fact that it does this so well given its complexity is a minor miracle. In this class, we'll study the structure of these individual networks and of the Internet, and figure out how this "magic" takes place. Topics include TCP/IP, protocols and their implementations, routing, security, network architecture, DNS, peertopeer networking, and WiFi along with ethical and privacy issues. Prerequisites: Computer Science 201 or instructor permission 6 credit; Formal or Statistical Reasoning; offered Fall 2015 · A. Csizmar Dalal 
CS 332: Operating Systems
The thing that we call a computer is actually a complex collection of interacting devices. To ensure that these devices work together effectively without excessive human intervention, people have developed operating systems software that coordinates the behavior of the devices and gives programmers ways to control those devices. This course will address the fundamental problems that operating systems need to solve, including those concerned with process management, file organization, memory management, and input/output control. We will also study the structure of the Linux operating system. Prerequisites: Computer Science 208 or instructor permission 6 credit; Formal or Statistical Reasoning; offered Spring 2016 · S. Goings 
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; not offered 2015–2016 
CS 338: Digital Electronics
Fun fact: Computers can be built up entirely from a collection of transistors. This course will begin the process of doing just that. From transistors we'll build logic gates. From logic gates, we'll build RAM and adders. With adders, we'll build arithmetic logic units, and so on, up to microprocessors. Prerequisites: Computer Science 208; Concurrent registration in Computer Science 338L 6 credit; Formal or Statistical Reasoning; offered Spring 2016 · A. Exley 
CS 342: Mobile Application Development
Software used to stay on the desktop where you put it. Now, we carry multipurpose computational devices in our pockets. Mobile computers raise a host of software design challenges, with constrained visual spaces, touch screens, GPS sensors, accelerometers, cellular access, and cameras all in one device. More challenges come from the idea of an "app store," a fiveyearold experiment that has changed the way developers and computer users think about software. In the context of a few app development projects, this course will focus on mobile computing's design patterns, user interface principles, software development methodologies, development tools, and cultural impact. Prerequisites: Computer Science 204 or 257 6 credit; Formal or Statistical Reasoning; not offered 2015–2016 
CS 344: HumanComputer Interaction
The field of humancomputer interaction addresses two fundamental questions: how do people interact with technology, and how can technology enhance the human experience? In this course, we will explore technology through the lens of the end user: how can we design effective, aesthetically pleasing technology, particularly user interfaces, to satisfy user needs and improve the human condition? How do people react to technology and learn to use technology? What are the social, societal, health, and ethical implications of technology? The course will focus on design methodologies, techniques, and processes for developing, testing, and deploying user interfaces. Prerequisites: Computer Science 201 or instructor permission 6 credit; Formal or Statistical Reasoning, Quantitative Reasoning Encounter; not offered 2015–2016 
CS 348: Parallel and Distributed Computing
As multicore machines become more prevalent, different programming paradigms have emerged for harnessing extra processors for better performance. This course explores parallel computation (programs that run on more than one core) as well as the related problem of distributed computation (programs that run on more than one machine). In particular, we will explore the two major paradigms for parallel programming, sharedmemory multithreading and messagepassing, and the advantages and disadvantages of each. Other possible topics include synchronization mechanisms, debugging concurrent programs, fork/join parallelism, the theory of parallelism and concurrency, parallel algorithms, cloud computing, Map/Reduce, GPU programming, transactional memory, and memory models. Prerequisites: Computer Science 201 6 credit; Formal or Statistical Reasoning; offered Winter 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; not offered 2015–2016 
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; not offered 2015–2016 
CS 362: Computational Biology
Recent advances in highthroughput experimental techniques have revolutionized how biologists measure DNA, RNA and protein. The size and complexity of the resulting datasets have led to a new era where computational methods are essential to answering important biological questions. This course focuses on the process of transforming biological problems into well formed computational questions and the algorithms to solve them. Topics include approaches to sequence comparison and alignment; molecular evolution and phylogenetics; DNA/RNA sequencing and assembly; and specific disease applications including cancer genomics. Prerequisites: Computer Science 201 and either Computer Science 202 or Mathematics 236, or instructor permission 6 credit; Formal or Statistical Reasoning; offered Winter 2016 · L. Oesper 
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; S/CR/NC; Does not fulfill a curricular exploration requirement; offered Fall 2015 · A. Exley, J. Ondich, S. Goings, A. Csizmar Dalal 
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 36 credit; S/NC; offered Fall 2015, Winter 2016 · A. Exley, J. Ondich, S. Goings, A. Csizmar Dalal