Courses

Many of the world's greatest discoveries and most consequential decisions are enabled or informed by the analysis of data from a myriad of sources. Indeed, the ability to wrangle, visualize, and draw conclusions from data is now a critical tool in the sciences, business, medicine, politics, other academic disciplines, and society as a whole. This course lays the foundations for quantifying relationships in data by exploring complementary computational, statistical, and visualization concepts. These concepts will be reinforced by lab experiences designed to teach programming and statistics skills while analyzing real-world data sets. This course will also examine the broader context and social issues surrounding data analysis, including privacy and ethics. [ more ]

This course introduces students to the science of computation by exploring the representation and manipulation of data and algorithms. We organize and transform information in order to solve problems using algorithms written in a modern object-oriented language. Topics include organization of data using objects and classes, and the description of processes using conditional control, iteration, methods and classes. We also begin the study of abstraction, self-reference, reuse, and performance analysis. While the choice of programming language and application area will vary in different offerings, the skills students develop will transfer equally well to more advanced study in many areas. In particular, this course is designed to provide the programming skills needed for further study in computer science and is expected to satisfy introductory programming requirements in other departments. [ more ]

This course builds on the programming skills acquired in Computer Science 134. It couples work on program design, analysis, and verification with an introduction to the study of data structures. Data structures capture common ways in which to store and manipulate data, and they are important in the construction of sophisticated computer programs. Students are introduced to some of the most important and frequently used data structures: lists, stacks, queues, trees, hash tables, graphs, and files. Students will be expected to write several programs, ranging from very short programs to more elaborate systems. Emphasis will be placed on the development of clear, modular programs that are easy to read, debug, verify, analyze, and modify. [ more ]

This course studies the basic instruction set architecture and organization of a modern computer. It provides a programmer's view of how computer systems execute programs, store information, and communicate. Over the semester the student learns the fundamentals of translating higher level languages into assembly language, and the interpretation of machine languages by hardware. At the same time, a model of computer hardware organization is developed from the gate level upward. [ more ]

This course investigates methods for designing efficient and reliable algorithms. By carefully analyzing the structure of a problem within a mathematical framework, it is often possible to dramatically decrease the computational resources needed to find a solution. In addition, analysis provides a method for verifying the correctness of an algorithm and accurately estimating its running time and space requirements. We will study several algorithm design strategies that build on data structures and programming techniques introduced in Computer Science 136. These include greedy, divide-and-conquer, dynamic programming, and network flow algorithms. Additional topics of study include algorithms on graphs and strategies for handling potentially intractable problems. [ more ]

This course will provide an overview of Computational Biology, the application of computational, mathematical, statistical, and physical problem-solving techniques to interpret the rapidly expanding amount of biological data. Topics covered will include database searching, DNA sequence alignment, clustering, RNA structure prediction, protein structural alignment, methods of analyzing gene expression, networks, and genome assembly using techniques such as string matching, dynamic programming, hidden Markov models, and statistics. [ more ]

This course examines the concepts and structures governing the design and implementation of programming languages. It presents an introduction to the concepts behind compilers and run-time representations of programming languages; features of programming languages supporting abstraction and polymorphism; and the procedural, functional, object-oriented, and concurrent programming paradigms. Programs will be required in languages illustrating each of these paradigms. [ more ]

This course explores different parallel programming paradigms used for writing applications on today's parallel computer systems. The course will introduce concurrency (i.e. multiple simultaneous computations) and the synchronization primitives that allow for the creation of correct concurrent applications. It will examine how a variety of systems organize parallel processing resources and enable users to write parallel programs for these systems. Covered programming paradigms will include multiprogramming with processes, message passing, threading in shared memory multiprocessors, vector processing, graphics processor programming, transactions, MapReduce, and other forms of programming for the cloud. Class discussion is based on assigned readings. Assignments provide students the opportunity to develop proficiency in writing software using different parallel programming paradigms. [ more ]

This course studies the key design principles of distributed systems, which are collections of independent networked computers that function as single coherent systems. Covered topics include communication protocols, processes and threads, naming, synchronization, consistency and replication, fault tolerance, and security. Students also examine some specific real-world distributed systems case studies, including Google and Amazon. Class discussion is based on readings from the textbook and research papers. The goals of this course are to understand how large-scale computational systems are built, and to provide students with the tools necessary to evaluate new technologies after the course ends. [ more ]

This course is a hands-on exploration of topics in robotics and digital fabrication. We will experience firsthand how ideas and methods from computer science can be applied to make physical objects, including robots and other machines. The emphasis will be on creative, hands-on experimentation. Along the way, students will learn the basics of embedded systems programming (Arduino), breadboarding, soldering, printed circuit board (PCB) design, mechanical computer-aided design (CAD)--both conventional (OnShape) and programmatic (OpenSCAD)--as well digital fabrication (3D-printing, laser cutting). Students will learn both how to build their own prototypes and how to send out designs to have parts machined professionally. Students will work in teams throughout. The course will culminate in a team robotic design competition testing both functionality and creativity. [ more ]

This course focuses on topics in game theory and mechanism design from a computational perspective. We will explore questions such as: how to design algorithms that incentivize truthful behavior, that is, where the participants have no incentive to cheat? Should we let drivers selfishly minimize their commute time or let a central algorithm direct traffic? Does Arrow's impossibility result mean that all voting protocols are doomed? The overarching goal of these questions is to understand and analyze selfish behavior and whether it can or should influence system design. Students will learn how to model and reason about incentives in computational systems both theoretically and empirically. Topics include types of equilibria, efficiency of equilibria, auction design and mechanism design with money, two-sided markets and mechanism design without money, incentives in computational applications such as P2P systems, and computational social choice. [ more ]

This course is about bridging the gap between theoretical running time and writing fast code in practice. The course is divided into two basic topics. The first is algorithmic: we will discuss some of the most useful tools in a coder's toolkit. This includes topics like randomization (hashing, filters, approximate counters), linear and convex programming, similarity search, and cache-efficient algorithms. Our goal is to talk about why these efficient algorithms make seemingly difficult problems solvable in practice. The second topic is applications: we will discuss how to implement algorithms in an efficient way that takes advantage of modern hardware. Specific topics covered will include blocking, loop unrolling, pipelining, as well as strategies for performance analysis. Projects and assessments will include both basic theoretical aspects (understanding why the algorithms we discuss actually work), and practical aspects (implementing the algorithms we discuss to solve important problems, and optimizing the code so it runs as quickly as possible). [ more ]

This course introduces a formal framework for investigating both the computability and complexity of problems. We study several models of computation including finite automata, regular languages, context-free grammars, and Turing machines. These models provide a mathematical basis for the study of computability theory--the examination of what problems can be solved and what problems cannot be solved--and the study of complexity theory--the examination of how efficiently problems can be solved. Topics include the halting problem and the P versus NP problem. [ more ]

Machine learning is a field that derives from artificial intelligence and statistics, and is concerned with the design and analysis of computer algorithms that "learn" automatically through the use of data. Computer algorithms are capable of discerning subtle patterns and structure in the data that would be practically impossible for a human to find. As a result, real-world decisions, such as treatment options and loan approvals, are being increasingly automated based on predictions or factual knowledge derived from such algorithms. This course explores topics in supervised learning (e.g., random forests and neural networks), unsupervised learning (e.g., k-means clustering and expectation maximization), and possibly reinforcement learning (e.g., Q-learning and temporal difference learning.) It will also introduce methods for the evaluation of learning algorithms (with an emphasis on analysis of generalizability and robustness of the algorithms to distribution/environmental shift), as well as topics in computational learning theory and ethics. [ more ]

Natural language processing (NLP) is a set of methods for making human language accessible to computers. NLP underlies many technologies we use on a daily basis including automatic machine translation, search engines, email spam detection, and automated personalized assistants. These methods draw from a combination of algorithms, linguistics and statistics. This course will provide a foundation in building NLP models to classify, generate, and learn from text data. [ more ]

Human-Computer Interaction (HCI) principles are practiced in the design and evaluation of most software, greatly impacting the lives of anyone who uses interactive technology and other products. There are many ways to design and build applications for people, so what methods can increase the likelihood that our design is the most useful, intuitive, and enjoyable? This course provides an introduction to the field of human-computer interaction, through a user-centered approach to designing and evaluating interactive systems. HCI draws on methods from computer science, the social and cognitive sciences, and interaction design. In this course we will use these methods to: ideate and propose design problems, study existing systems and challenges, explore design opportunities and tradeoffs, evaluate and improve designs, and communicate design problems and solutions to varying audiences. [ more ]

This course is an introduction to deep neural networks and how to train them. Beginning with the fundamentals of regression and optimization, the course then surveys a variety of neural network architectures, which may include multilayer feedforward neural networks, convolutional neural networks, recurrent neural networks, and transformer networks. Students will also learn how to use deep learning software such as PyTorch or Tensorflow. [ more ]

This course provides highly-motivated students an opportunity to work independently with faculty on research topics chosen by individual faculty. Students are generally expected to perform a literature review, identify areas of potential contribution, and explore extensions to existing results. The course culminates in a concise, well-written report describing a problem, its background history, any independent results achieved, and directions for future research. [ more ]

Computer Science thesis; this is part of a full-year thesis (493-494). [ more ]

Directed independent reading in Computer Science. [ more ]

Directed independent reading in Computer Science. [ more ]