 2020–2021 Courses:
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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.
6 credits; Formal or Statistical Reasoning, Quantitative Reasoning Encounter; offered Fall 2020, Winter 2021, Spring 2021 · Sneha Narayan, Eric Alexander, David LibenNowell, Layla Oesper, Anya Vostinar, Amy Csizmar Dalal, David Musicant 
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 credits; Formal or Statistical Reasoning, Quantitative Reasoning Encounter; offered Fall 2020, Winter 2021, Spring 2021 · Anya Vostinar, Anna Rafferty, David Musicant, Aaron Bauer, Sneha Narayan 
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 credits; Formal or Statistical Reasoning; offered Winter 2021, Spring 2021 · Layla Oesper 
CS 208: Introduction to Computer Systems
Are you curious what’s really going on when a computer runs your code? In this course we will demystify the machine and the tools that we use to program it. Our broad survey of how computer systems execute programs, store information, and communicate will focus on the hardware/software interface, including data representation, instruction set architecture, the C programming language, memory management, and the operating system process model.
Prerequisites: Computer Science 111 or instructor permission 6 credits; Formal or Statistical Reasoning; offered Fall 2020, Spring 2021 · Aaron Bauer 
CS 231: Computer Security
Hackers, phishers, and spammers–at 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 credits; Formal or Statistical Reasoning; offered Spring 2021 · Jeffrey Ondich 
CS 232: Art, Interactivity, and Robotics
In this handson studio centered course, we’ll explore and create interactive three dimensional art. Using basic construction techniques, microprocessors, and programming, this class brings together computer science, sculpture, engineering, and aesthetic design. Students will engage the nutsandbolts of fabrication, learn to program microcontrollers, and study the design of interactive constructions. Collaborative labs and individual projects will culminate in a campus wide exhibition. No prior building experience is required.
Prerequisites: Computer Science 111. Not open to students who taken previous offering of Art, Interactivity and Robotics 6 credits; Arts Practice; not offered 2020–2021 
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 credits; Formal or Statistical Reasoning; offered Fall 2020, Winter 2021, Spring 2021 · David Musicant, Anna Rafferty 
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 Computer Science 202 (Mathematics 236 will be accepted in lieu of Computer Science 202) 6 credits; Formal or Statistical Reasoning; offered Fall 2020, Winter 2021, Spring 2021 · Layla 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 201 and Computer Science 202 (Mathematics 236 will be accepted in lieu of Computer Science 202) 6 credits; Formal or Statistical Reasoning; offered Fall 2020, Winter 2021 · James Ryan, Josh Davis 
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 credits; Formal or Statistical Reasoning; offered Fall 2020, Winter 2021, Spring 2021 · Jeffrey Ondich 
CS 298: Reading and Analysis Associated with External Computing Experience
An independent study course intended for students who require Curricular Practical Training (CPT) or Optional Practical Training (OPT) to go with an external activity related to computer science (for example, an internship or an externship). The student will choose and read academic material relating to a practical experience (e.g., internship), and write a paper describing what the student learned from the reading, and how it related to the practical experience.
Prerequisites: Instructor’s permission 1 credit; Does not fulfill a curricular exploration requirement; not offered 2020–2021 
CS 301: History of Computing in England Program: History of Computing
In the mid1800s, Charles Babbage’s analytical engine, inspired by programmable looms, was the first conception of an automated programmable computing device. A century later, British researchers built some of the first physical computers—particularly WWIIera codebreaking work, and programmable machines developed immediately after the war. We will explore those two eras, through historical writings (including Babbage and Ada Lovelace, who wrote programs for the analytical engine, and Alan Turing) and visits to relevant museums and archives. We will also study some of the more recent history of computing, particularly the major advances in the 1960s and 1970s.
Prerequisites: Computer Science 201 and 202 (Math 236 will be accepted in lieu of Computer Science 202) 6 credits; Formal or Statistical Reasoning; not offered 2020–2021 
CS 304: Social Computing
The last decade has seen a vast increase in the number of applications that connect people with one another. This course presents an interdisciplinary introduction to social computing, a field of study that explores how computational techniques and artifacts are used to support and understand social interactions. We will examine a number of sociotechnical systems (such as wikis, social media platforms, and citizen science projects), discuss the design principles used to build them, and analyze how they help people mobilize and collaborate with one another. Assignments will involve investigating datasets from online platforms and exploring current research in the field.
Prerequisites: Computer Science 201 6 credits; Formal or Statistical Reasoning, Quantitative Reasoning Encounter; offered Fall 2020 · Sneha Narayan 
CS 311: Computer Graphics
Scientific simulations, movies, and video games often incorporate computergenerated images of fictitious worlds. How are these worlds represented 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 threedimensional computer graphics, including both triangle rasterization and ray tracing. Familiarity with vectors, matrices, and the C programming language is recommended but not required.
Prerequisites: Computer Science 201 6 credits; Quantitative Reasoning Encounter, Formal or Statistical Reasoning; not offered 2020–2021 
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. During the Digital Audio portion of the course, you’ll learn the basics of audio synthesis: oscillators, envelopes, filters, amplifiers, and FFT analysis. Weekly homework assignments, two quizzes, and two independent projects.
Prerequisites: Computer Science 201 or instructor permission 6 credits; Formal or Statistical Reasoning; not offered 2020–2021 
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 credits; Formal or Statistical Reasoning, Quantitative Reasoning Encounter; offered Winter 2021 
CS 318: Computational Media
How does computation enable new forms of creative expression? What kinds of media artifacts and experiences can only happen on computers? In this course, we’ll explore these notions through a handson survey of various forms of computational media, such as: computer simulation, computergenerated visual art, poetry generation, story generation, chatbots, Twitter bots, explorable explanations, and more. For each topic in the survey, students will learn about the past, present, and future of a given form through short readings and direct engagement with major works. Assignments and a final project will center on the creation of novel media artifacts and also reimplementations of lost or defunct historical programs.
Prerequisites: Computer Science 111 or 201 6 credits; Does not fulfill a curricular exploration requirement; offered Fall 2020 · James Ryan 
CS 320: Machine Learning
What does it mean for a machine to learn? Much of modern machine learning focuses on identifying patterns in large datasets and using these patterns to make predictions about the future. Machine learning has impacted a diverse array of applications and fields, from scientific discovery to healthcare to education. In this artificial intelligencerelated course, we’ll both explore a variety of machine learning algorithms in different application areas, taking both theoretical and practical perspectives, and discuss impacts and ethical implications of machine learning more broadly. Topics may vary, but typically focus on regression and classification algorithms, including neural networks.
Prerequisites: Computer Science 201 and Computer Science 202 (Mathematics 236 will be accepted in lieu of Computer Science 202) 6 credits; Formal or Statistical Reasoning; offered Fall 2020 · Anna Rafferty 
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 is strongly recommended. 6 credits; Formal or Statistical Reasoning; not offered 2020–2021 
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 Computer Science 202 (Mathematics 236 will be accepted in lieu of Computer Science 202) 6 credits; Formal or Statistical Reasoning, Quantitative Reasoning Encounter; offered Spring 2021 · Anna Rafferty 
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 is strongly recommended 6 credits; Formal or Statistical Reasoning, Quantitative Reasoning Encounter; not offered 2020–2021 
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 credits; Formal or Statistical Reasoning, Quantitative Reasoning Encounter; not offered 2020–2021 
CS 330: Introduction to RealTime Systems
How can we prove that dynamic cruise control will brake quickly enough if traffic suddenly stops? How must a system coordinate processes to detect pedestrians and other vehicles to ensure fair sharing of computing resources? In realtime systems, we explore scheduling questions like these, which require provable guarantees of timing constraints for applications including autonomous vehicles. This course will start by considering such questions for uniprocessor machines, both when programs have static priorities and when priorities can change over time. We will then explore challenges introduced by modern computers with multiple processors. We will consider both theoretical and practical perspectives.
Prerequisites: Computer Science 201 and Computer Science 202 (Mathematics 236 will be accepted in lieu of Computer Science 202). 6 credits; Formal or Statistical Reasoning; not offered 2020–2021 
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 credits; Formal or Statistical Reasoning; offered Fall 2020 · Amy Csizmar Dalal 
CS 332: Operating Systems
If you’re working in the lab, you might be editing a file while waiting for a program to compile. Meanwhile, the onscreen clock ticks, a program keeps watch for incoming email, and other users can log onto your machine from elsewhere in the network. Not only that, but if you write a program that reads from a file on the hard drive, you are not expected to concern yourself with turning on the drive’s motor or moving the read/write arms to the proper location over the disk’s surface. Coordinating all this hardware and software is the job of the operating system. In this course we will study the fundamentals of operating system design, including the operating system kernel, scheduling and concurrency, memory management, and file systems.
Prerequisites: Computer Science 201 and 208 or instructor permission 6 credits; Formal or Statistical Reasoning; not offered 2020–2021 
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 credits; Formal or Statistical Reasoning; offered Spring 2021 · Aaron Bauer 
CS 341: History of Computing in England Program: 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. Topics will include publickey encryption, digital signatures, codebreaking techniques (like those used at Bletchley Park during WWII to break the Enigma machine’s cryptosystem), pseudorandom number generation, and other cryptographic applications.
Prerequisites: Computer Science 201 and 202. (Mathematics 236 will be accepted in lieu of CS 202) 6 credits; Formal or Statistical Reasoning, Quantitative Reasoning Encounter; not offered 2020–2021 
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 credits; Formal or Statistical Reasoning, Quantitative Reasoning Encounter; not offered 2020–2021 
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, and Map/Reduce.
Prerequisites: Computer Science 201 6 credits; Formal or Statistical Reasoning; offered Winter 2021 · David 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 credits; Formal or Statistical Reasoning; not offered 2020–2021 
CS 358: Quantum Computing
Quantum computing is a promising technology that may (or may not) revolutionize computer science over the next few decades. By exploiting quantum phenomena such as superposition and entanglement, quantum computers can solve problems in a fundamentally different way from that of conventional computers. This course surveys the computer science and mathematics of quantum algorithms, including Shor’s and Grover’s algorithms, error correction, and cryptography. No prior experience with quantum theory is needed.
Prerequisites: Computer Science 201, Mathematics 232, and Computer Science 202 (Mathematics 236 will be accepted in lieu of Computer Science 202) 6 credits; Formal or Statistical Reasoning; not offered 2020–2021 
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 credits; Formal or Statistical Reasoning; offered Winter 2021 · Anya Vostinar 
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 Computer Science 202 (Mathematics 236 will be accepted in lieu of Computer Science 202) 6 credits; Formal or Statistical Reasoning, Quantitative Reasoning Encounter; not offered 2020–2021 
CS 364: Molecular Programming and Nanoscale SelfAssembly
Algorithms are ubiquitous in nature and are even present in biological and chemical processes. For example, cells receive molecular signals, execute procedures, and send signals of their own, and chemical reactions compute functions by transforming reactants into products according to molecular rules. In this course, we will investigate various mathematical models of chemistry, biology, and nanoscale selfassembly. We will use each model as a programming language to compute molecular algorithms, verify their correctness, and analyze their complexity and robustness. We will also discover that many of these models are algorithmically universal and are equivalent in power to modern programming languages.
Prerequisites: Computer Science 201 and Computer Science 202 (Mathematics 236 will be accepted in lieu of Computer Science 202). No background in biology or chemistry is required, but it may be helpful 6 credits; Formal or Statistical Reasoning, Quantitative Reasoning Encounter; not offered 2020–2021 
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 Computer Science 257 before starting Computer Science 399. 3 credits; S/CR/NC; Does not fulfill a curricular exploration requirement; offered Fall 2020, Winter 2021 · Amy Csizmar Dalal, Aaron Bauer, Sneha Narayan, Jeffrey Ondich 
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 credits; S/NC; offered Winter 2021, Spring 2021 · Amy Csizmar Dalal, Aaron Bauer, Sneha Narayan, Jeffrey Ondich