Courses

The internet is an essential technology that touches virtually every aspect of our lives. Since its introduction in the early 80s, it has grown widely and transformed how we work, learn, and communicate. How does this critical piece of global infrastructure work, and how is it governed? What implications did its arrival have on how we interact with one another and participate in society? In this course, we will explore topics related to the structure and origins of the internet, the legal frameworks and business models that shape how we experience it, its transformation of the public sphere, and its potential to both reduce and exacerbate inequalities.

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 object-oriented 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.

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.

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.

In this hands-on course, taught (in an art studio) by a sculpture professor and computer science professor, we’ll explore and create interactive three dimensional art. Using basic construction techniques, microprocessors, and programming, this class brings together sculpture, engineering, computer science, and aesthetic design. Students will engage the nuts and bolts 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.

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.

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.

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 finite-state automata, pushdown automata, and Turing machines; formal languages, including regular expressions and context-free grammars; computability and uncomputability; and computational complexity, particularly NP-completeness.

Students earn credit by attending at least five of the research-based events in the Computer Science department’s weekly colloquium series. Speakers come from academia, industry, nonprofits, and government, and present on a variety of topics, within and adjacent to computer science. Students will submit brief written reports after each talk that they attend.

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.

In the mid-1800s, 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 WWII-era code-breaking 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.

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 socio-technical 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.

Scientific simulations, movies, and video games often incorporate computer-generated 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 trade-offs come into play? In this course we learn the basic theory and methodology of three-dimensional computer graphics, including both triangle rasterization and ray tracing. Familiarity with vectors, matrices, and the C programming language is recommended but not required.

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.

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, human-computer 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.

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 hands-on survey of various forms of computational media, such as: computer simulation, computer-generated 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.

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 intelligence-related 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.

There are many situations where computer systems must make intelligent choices, from selecting actions in a game, to suggesting ways to distribute scarce resources for monitoring endangered species, to a search-and-rescue robot learning to interact with its environment. Artificial intelligence offers multiple frameworks for solving these problems. While popular media attention has often emphasized supervised machine learning, this course instead engages with a variety of other approaches in artificial intelligence, both established and cutting edge. These include intelligent search strategies, game playing approaches, constrained decision making, reinforcement learning from experience, and more. Coursework includes problem solving and programming.

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.

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 real-time 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.

If you’re working in the lab, you might be editing a file while waiting for a program to compile. Meanwhile, the on-screen clock ticks, a program keeps watch for incoming e-mail, 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.

When hackers can disable gas pipelines, national hospital systems, and electrical grids, and data brokers can create a largely unregulated world-wide surveillance system, there’s a clear need for people who understand the mechanisms of computer security and insecurity. Towards that end, in this course we will study technical and social aspects of computer and network security. Topics will include threat modeling, cryptography, secure network protocols, web security, ethical hacking and penetration testing, authentication, authorization, historical hacking incidents, usability, privacy, and security-related law.

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, password-protected 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 public-key encryption, digital signatures, code-breaking techniques (like those used at Bletchley Park during WWII to break the Enigma machine’s cryptosystem), pseudorandom number generation, and other cryptographic applications.

As multi-core machines become more prevalent, different programming paradigms have emerged for harnessing extra processors for better performance. This course explores parallel computation for both shared memory and distributed parallel programming paradigms. In particular, we will explore how these paradigms affect the code we write, the libraries we use, and the advantages and disadvantages of each. Topics will include synchronization primitives across these models for parallel execution, debugging concurrent programs, fork/join parallelism, example parallel algorithms, computational complexity and performance considerations, computer architecture as it relates to parallel computation, and related theory topics.

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.

The field of artificial life seeks to understand the dynamics of life by separating them from the substrate of DNA. In this course, we will explore how we can implement the dynamics of life in software to test and generate biological hypotheses, with a particular focus on evolution. Topics will include the basic principles of biological evolution, transferring experimental evolution techniques to computational systems, cellular automata, computational modeling, and digital evolution. All students will be expected to complete and present a term research project recreating and extending recent work in the field of artificial life.

Recent advances in high-throughput 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.

As part of their senior capstone experience, majors will work together in teams (typically four to seven students per team) on faculty-specified topics to design and implement the first stage of a project. Required of all senior majors.