What are the theoretical limits of computing power? What problems can be solved with computers? Which ones cannot? And how can we reason about the answers to these questions with mathematical certainty? This course explores the answers to these questions and serves as an introduction to discrete mathematics, computability theory, and complexity theory. At the completion of the course, students will feel comfortable writing mathematical proofs, reasoning about discrete structures, reading and writing statements in first-order logic, and working with mathematical models of computing devices. Throughout the course, students will gain exposure to some of the most exciting mathematical and philosophical ideas of the late nineteenth and twentieth centuries. Specific topics covered include formal mathematical proofwriting, propositional and first-order logic, set theory, binary relations, functions (injections, surjections, and bijections), cardinality, basic graph theory, the pigeonhole principle, mathematical induction, finite automata, regular expressions, the Myhill-Nerode theorem, context-free grammars, Turing machines, decidable and recognizable languages, self-reference and undecidability, verifiers, and the P versus NP question. Students with significant proofwriting experience are encouraged to instead take CS154. Students interested in extra practice and support with the course are encouraged to concurrently enroll in CS103A. CS106B may be taken concurrently with CS103.

May be taken for 3 or 4 units by Stanford graduate students.

Introduction to the engineering of computer applications emphasizing modern software engineering principles: program design, decomposition, encapsulation, abstraction, and testing. Emphasis is on good programming style and the built-in.

Do not enroll in discussion sections in Axess. The first week of class, students sign up for discussion sections via CS198 website. Discussion section assignments received during second week of class when sections start.

Abstraction and its relation to programming. Software engineering principles of data abstraction and modularity. Object-oriented programming, fundamental data structures (such as stacks, queues, sets) and data-directed design. Recursion and recursive data structures (linked lists, trees, graphs). Introduction to time and space complexity analysis. Uses the programming language C++ covering its basic facilities.

Do not enroll in discussion sections in Axess. The first week of class, students sign up for discussion sections via CS198 website. Discussion section assignments received during second week of class when sections start.

Introduction to the fundamental concepts of computer systems. Explores how computer systems execute programs and manipulate data, working from the C programming language down to the microprocessor. Topics covered include: the C programming language, data representation, machine-level code, computer arithmetic, elements of code compilation, memory organization and management, and performance evaluation and optimization.

May be taken for 3 or 4 units by Stanford grad students.

Topics include: counting and combinatorics, random variables, conditional probability, independence, distributions, expectation, point estimation, and limit theorems. Applications of probability in computer science including machine learning and the use of probability in the analysis of algorithms.

May be taken for 3 or 4 units by Stanford grad students.

Worst and average case analysis. Recurrences and asymptotics. Efficient algorithms for sorting, searching, and selection. Data structures: binary search trees, heaps, hash tables. Algorithm design techniques: divide-and-conquer, dynamic programming, greedy algorithms, amortized analysis, randomization. Algorithms for fundamental graph problems: minimum-cost spanning tree, connected components, topological sort, and shortest paths. Possible additional topics: network flow, string searching.

May be taken for 3 or 4 units by Stanford grad students.

Client-side technologies used to create web sites such as Google maps or Gmail. Includes HTML5, CSS, JavaScript, the Document Object Model (DOM), and Ajax.

Can racial diversity on college campuses be achieved without affirmative action? This is one of many questions that students, college admissions officers, college counselors, teachers, and university staff have on their minds after the Students for Fair Admissions Inc v Harvard College and University of North Carolina-Chapel Hill Supreme Court cases in 2023 outlawed the use of race-based considerations within United States college admissions. This course takes a multidisciplinary approach and includes works from sociology, history, education, ethnic studies, and legal studies to learn about how race has been considered within U.S. college admissions and what the future may hold for race-based considerations in the higher education landscape. Students can expect to learn more about the history that led up to the rise and fall of affirmative action in higher education admissions, how U.S. college admissions varies across type of institution and selectivity, and why diversity and equity is important for higher education. Although the course has a focus on the U.S. higher education system, there are opportunities for students to explore what higher education admissions looks like in other countries and contexts as well as how international students and non-U.S. citizens are impacted by race-based considerations upon applying to U.S. colleges and universities. It is important to note that this is not a course that will prepare students to apply to college but rather is a class for anyone interested in racial justice, interdisciplinary social science, or higher education.

This course explores the intersections of race, gender, and humor within various cultural and historical contexts. It examines how humor can reflect and shape ideas about race and gender, how it can both challenge and reinforce stereotypes, and how it operates in different media and genres as a form of resistance and oppression. Students will engage with theories of humor, critical race theory, and gender studies to critically analyze texts, performances, and media.

Once you get into the school of your dreams, how will you be sure you can succeed there? The level of organization and study skills necessary for college success are often very different than in high school. In Working Smarter, you will learn evidence-based, college-level strategies for time management, note taking, studying, reading, writing, discussion, and oral presentations. This class is a great fit for high school students who want to prepare for college and for college students who want to expand their set of strategies for successful learning in STEM, social science, and humanities courses.