91 - 100 of 366 results for: CS

Trust and Safety is the field of professional and academic effort to allow people to positively use technology while being safe from harm. This course explores how online services and AI systems are abused to cause real human harm and the potential social, operational, product, legal and engineering responses. Students will learn about the use of social media by terrorists, online fraud, pig-butchering, misinformation, child exploitation, harassment, self-harm and AI psychosis. This will include studying both the technical and sociological roots of these harms and the ways various companies have responded. The class is taught by a practitioner with a CS background and a professor of communication with a psychology background and supplemented by guest lecturers from tech companies. Students will spend the quarter working in teams to build solutions to real trust and safety challenges, which will include the application of AI technologies to detecting and stopping abuse. For those taking this course for CS credit, the prerequisite is CS106B or equivalent and this course fulfills the Technology in Society requirement. Content note: This class will cover real-world harmful behavior and expose students to potentially upsetting material. Course website: https://cs152.stanford.edu/

The AI stack is being rebuilt from the ground up, from land and power to chips, cloud, models, and applications. This course walks you through every layer with the leaders building each one. Past speakers include Jensen Huang (NVIDIA), Greg Brockman (OpenAI), Ben Mann (Anthropic), Guillaume Lample (Mistral/Llama), Robin Rombach (Stable Diffusion/BFL), Matthew Prince (Cloudflare), Sualeh Asif (Cursor), Shyam Sankar (Palantir), HE Abdullah Al Swaha (KSA) among others

This course provides a mathematical introduction to the following questions: What is computation? Given a computational model, what problems can we hope to solve in principle with this model? Besides those solvable in principle, what problems can we hope to efficiently solve? In many cases we can give completely rigorous answers; in other cases, these questions have become major open problems in computer science and mathematics. By the end of this course, students will be able to classify computational problems in terms of their computational complexity (Is the problem regular? Not regular? Decidable? Recognizable? Neither? Solvable in P? NP-complete? PSPACE-complete?, etc.). Students will gain a deeper appreciation for some of the fundamental issues in computing that are independent of trends of technology, such as the Church-Turing Thesis and the P versus NP problem. Prerequisites: CS 103 or 103B.

For juniors, seniors, and first-year graduate students. Principles of computer systems security. Attack techniques and how to defend against them. Topics include: network attacks and defenses, operating system security, application security (web, apps, databases), malware, privacy, and security for mobile devices. Course projects focus on building reliable software. Prerequisite: 110. Recommended: basic Unix.

Rigorous introduction to Symbolic Logic from a computational perspective. Encoding information in the form of logical sentences. Reasoning with information in this form. Overview of logic technology and its applications - in mathematics, science, engineering, business, law, and so forth. Topics include the syntax and semantics of Propositional Logic, Relational Logic, and Herbrand Logic, validity, contingency, unsatisfiability, logical equivalence, entailment, consistency, natural deduction (Fitch), mathematical induction, resolution, compactness, soundness, completeness.

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. Prerequisite: 106B or 106X; 103 or 103B; 109 or STATS 116.

Additional problem solving practice for CS161. Sections are designed to allow students to acquire a deeper understanding of CS and its applications, work collaboratively, and develop a mastery of the material. Concurrent enrollment in CS 161 required. Limited enrollment, permission of instructor, and application required.

(Previously numbered CS 353). Introduction to research in the Theory of Computing, with an emphasis on research methods (the practice of research), rather than on any particular body of knowledge. The students will participate in a highly structured research project: starting from reading research papers from a critical point of view and conducting bibliography searches, through suggesting new research directions, identifying relevant technical areas, and finally producing and communicating new insights. The course will accompany the projects with basic insights on the main ingredients of research. Research experience is not required, but basic theory knowledge and mathematical maturity are expected. The target participants are advanced undergrads as well as MS students with interest in CS theory. Prerequisites: CS161 and CS154. Limited class size.

This course is a deep dive into the design, analysis, implementation, and theory of data structures. Over the course of the quarter, we'll explore fundamental techniques in data structure design (isometries, amortization, randomization, etc.), as well as perspectives and intuitions useful for developing new data structures. By the time we've finished, we'll have seen some truly beautiful strategies for solving problems efficiently. Possible topics include Fibonacci heaps, fusion trees, succinct rank and select, B-trees, range minimum queries, suffix trees, dynamic connectivity, cuckoo hashing, and planar point location. Prerequisites: CS107 and CS161.