An introduction to computational problem solving designed to give an overview of computer science using real-world problems across a broad range of disciplines. Students learn how to think about these problems and how to structure effective solutions to them using computation. No programming knowledge is required or expected; students learn how to implement their solutions in Python. If you register for fully online section, you must have a webcam and you must take the exams in person.

Algorithms are the engines of a great majority of systems, natural and artificial alike. This course introduces algorithmic thinking as a discipline for reasoning about systems, taming their complexities, and elucidating their properties. Algorithmic techniques, along with their correctness and efficiency, will be taught through reasoning about systems of interactions, such as markets, that are ubiquitous in our highly connected world.

This course covers the principles of programming and program design. The course is organized around a number of individual programming assignments that fit together to complete a significant, real-world application. Each assignment emphasizes one or more of the basic principles of software design, including: encapsulation, abstraction, test-driven development, and functional and object-oriented programming. The Java programming language will be used. An introduction to the basics of the Java language itself (including Java syntax and semantics) will be provided.

This course introduces students to the organization of computer systems in order that they gain an understanding of how a computer system executes their programs. Students will learn how to write small-scale programs in C, how to read the translation of those programs into assembly language, and how those programs are executed at the machine level. Specific topics covered will include data representation at the machine level, static versus dynamic memory allocation, instruction set architecture and the encoding of instructions in memory, linking relocatable object files to create executable files, pipelining within the processor, and caching within the memory system.

Given their growing power in the twenty-first century, computer scientists have duties both to society and their own profession to wield that power wisely and responsibly. In this discussion-and reflection-oriented course students will apply fundamentals of moral philosophy and social responsibility to current issues in computer science.

This course explores the design landscape of programming languages and compilers, with the primary goal of giving students skills and knowledge that will help them as practitioners. After completing the course, students should be able to evaluate the tradeoffs between different programming languages, choose the language that is best-suited to a given task, write code using the functional programming paradigm, and learn new languages more quickly. Specific topics covered include lexical and syntactic analysis, type systems, variable scopes and bindings, control flow structures, and functional programming. Case studies from modern programming languages will be used to illustrate a range of abstractions and design choices.

Modern software systems are typically complex, event-driven, and require coordination across multiple components. Such systems require careful design to ensure that they uphold best practices in software design while supporting concurrency. This course will introduce principles of designing large-scale concurrent software and give students practice implementing these principles in the context of large-scale, highly concurrent software systems. Topics covered will include concurrency vs. parallelism, concurrency concepts and mechanisms, and principles of software design including encapsulation, composition, decoupling, and accessibility.

This course introduces computer systems from the programmer's perspective. Topics include data representation, the compilation process, and system-level programming concepts such as interrupts and concurrency. Formerly COMP 221.

Writing algorithms is fun, but how are you sure that the algorithm you wrote is flawless? Are there computing tasks for which it is impossible to produce an efficient algorithm, or, for that matter, any algorithm? To answer these questions, you have to learn to perform mathematical reasoning about algorithmic problems and solutions COMP 382 is an introduction to such reasoning techniques. Topics covered would include elementary logic, analysis of the correctness and efficiency of algorithms, and formal computational models like finite automata and Turning machines. On the way, you are also going to learn some new algorithm design techniques.