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151 - 160 of 217 results for: CS ; Currently searching offered courses. You can also include unoffered courses

CS 278: Social Computing (SOC 174, SOC 274)

Today we interact with our friends and enemies, our team partners and romantic partners, and our organizations and societies, all through computational systems. How do we design these social computing systems - platforms for social media, online communities, and collaboration - to be effective and responsible? This course covers design patterns for social computing systems and the foundational ideas that underpin them.
Terms: Spr | Units: 3-4

CS 279: Computational Biology: Structure and Organization of Biomolecules and Cells (BIOE 279, BIOMEDIN 279, BIOPHYS 279, CME 279)

Computational techniques for investigating and designing the three-dimensional structure and dynamics of biomolecules and cells. These computational methods play an increasingly important role in drug discovery, medicine, bioengineering, and molecular biology. Course topics include protein structure prediction, protein design, drug screening, molecular simulation, cellular-level simulation, image analysis for microscopy, and methods for solving structures from crystallography and electron microscopy data. Prerequisites: elementary programming background ( CS 106A or equivalent) and an introductory course in biology or biochemistry.
Terms: Aut | Units: 3

CS 281: Ethics of Artificial Intelligence

Machine learning has become an indispensable tool for creating intelligent applications, accelerating scientific discoveries, and making better data-driven decisions. Yet, the automation and scaling of such tasks can have troubling negative societal impacts. Through practical case studies, you will identify issues of fairness, justice and truth in AI applications. You will then apply recent techniques to detect and mitigate such algorithmic biases, along with methods to provide more transparency and explainability to state-of-the-art ML models. Finally, you will derive fundamental formal results on the limits of such techniques, along with tradeoffs that must be made for their practical application. CS229 or equivalent classes or experience.
Terms: Spr | Units: 3-4
Instructors: Guestrin, C. (PI)

CS 288: Applied Causal Inference with Machine Learning and AI (MS&E 228)

Fundamentals of modern applied causal inference. Basic principles of causal inference and machine learning and how the two can be combined in practice to deliver causal insights and policy implications in real world datasets, allowing for high-dimensionality and flexible estimation. Lectures will provide foundations of these new methodologies and the course assignments will involve real world data (from social science, tech industry and healthcare applications) and synthetic data analysis based on these methodologies. Prerequisites: basic knowledge of probability and statistics. Recommended: 226 or equivalent.
Terms: Win | Units: 3

CS 293: Empowering Educators via Language Technology (EDUC 473)

This course explores the use of natural language processing (NLP) to support educators, by discovering, measuring, and analyzing high-leverage teaching practices. Topics include computational social science methods, ethics, bias and fairness, automated scoring, causal analyses, large language models, among others. Engaging with relevant papers, students will work towards a final project using NLP methods and a critical social scientific lens. Projects are pitched to a jury of educators at the end of the course.
Terms: Aut | Units: 2-4

CS 295: Software Engineering

Software specification, testing and verification. The emphasis is on automated tools for developing reliable software. The course covers material---drawn primarily from recent research papers---on the technologyunderlying these tools. Assignments supplement the lectures with hands-on experience in using these tools and customizing them for solving new problems. The course is appropriate for students intending to pursue research in program analysis and verification, as well as for those who wish to add the use of advanced software tools to their skill set. Prerequisites: 108. Recommended: a project course such as 140, 143 or 145.
Terms: Win | Units: 3

CS 298: Seminar on Teaching Introductory Computer Science (EDUC 298)

Faculty, undergraduates, and graduate students interested in teaching discuss topics raised by teaching computer science at the introductory level. Prerequisite: consent of instructor.
Terms: Aut | Units: 1
Instructors: Gregg, C. (PI)

CS 300: Departmental Lecture Series

Priority given to first-year Computer Science Ph.D. students. CS Masters students admitted if space is available. Presentations by members of the department faculty, each describing informally his or her current research interests and views of computer science as a whole.
Terms: Aut | Units: 1
Instructors: Reingold, O. (PI)

CS 309A: Cloud Computing Seminar

For science, engineering, computer science, business, education, medicine, and law students. Cloud computing is bringing information systems out of the back office and making it core to the entire economy. Furthermore with the advent of smarter machines cloud computing will be integral to building a more precision planet. This class is intended for all students who want to begin to understand the implications of this technology. Guest industry experts are public company CEOs who are either delivering cloud services or using cloud services to transform their businesses.
Terms: Aut | Units: 1 | Repeatable for credit
Instructors: Chou, T. (PI)

CS 323: The AI Awakening: Implications for the Economy and Society

Intelligent computer agents must reason about complex, uncertain, and dynamic environments. This course is a graduate level introduction to automated reasoning techniques and their applications, covering logical and probabilistic approaches. Topics include: logical and probabilistic foundations, backtracking strategies and algorithms behind modern SAT solvers, stochastic local search and Markov Chain Monte Carlo algorithms, variational techniques, classes of reasoning tasks and reductions, and applications. Enrollment by application: https://digitaleconomy.stanford.edu/about/the-ai-awakening-implications-for-the-economy-and-society/
Terms: Spr | Units: 3-4
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