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71 - 80 of 100 results for: CS

CS 277: Foundation Models for Healthcare (BIODS 271, RAD 271)

Generative AI and large-scale self-supervised foundation models are poised to have a profound impact on human decision making across occupations. Healthcare is one such area where such models have the capacity to impact patients, clinicians, and other care providers. In this course, we will explore the training, evaluation, and deployment of generative AI and foundation models, with a focus on addressing current and future medical needs. The course will cover models used in natural language processing, computer vision, and multi-modal applications. We will explore the intersection of models trained on non-healthcare domains and their adaptation to domain-specific problems, as well as healthcare-specific foundation models. Prerequisites: Familiarity with machine learning principles at the level of CS 229, 231N, or 224N
Terms: Win | Units: 3

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 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 324H: History of Natural Language Processing

Intellectual history of computational linguistics, natural language processing, and speech recognition, using primary sources. Reading of seminal early papers, interviews with early pioneers, with the goal of understanding the origins and intellectual development of the field. Prerequisites: (strictly required) completion of a Stanford graduate NLP course ( CS 224C/N/U/S, 329X, 384).
Terms: Win | Units: 3-4

CS 339R: Collaborative Robotics (ME 326)

This course focuses on how robots can be effective teammates with other robots and human partners. Concepts and tools will be reviewed for characterizing task objectives, robot perception and control, teammate behavioral modeling, inter-agent communication, and team consensus. We will consider the application of these tools to robot collaborators, wearable robotics, and the latest applications in the relevant literature. This will be a project-based graduate course, with the implementation of algorithms in either python or C++.
Terms: Win | Units: 3

CS 342: Building for Digital Health (MED 253)

This project-based course will provide a comprehensive overview of key requirements in the design and full-stack implementation of a digital health research application. Several pre-vetted and approved projects from the Stanford School of Medicine will be available for students to select from and build. Student teams learn about all necessary approval processes to deploy a digital health solution (data privacy clearance/I RB approval, etc.) and be guided in the development of front-end and back-end infrastructure using best practices. The final project will be the presentation and deployment of a fully approved digital health research application. CS106A, CS106B, Recommended: CS193P/A, CS142, CS47, CS110. Limited enrollment for this course. Apply for enrollment permission here: https://stanforduniversity.qualtrics.com/jfe/form/SV_9ThVhqf4zyhzheS
Terms: Win | Units: 3-4

CS 343D: Domain-Specific Programming Models and Compilers

This class will cover the principles and practices of domain-specific programming models and compilers for dense and sparse applications in scientific computing, data science, and machine learning. We will study programming models from the recent literature, categorize them, and discuss their properties. We will also discuss promising directions for their compilation, including the separation of algorithm, schedule, and data representation, polyhedral compilation versus rewrite rules, and sparse iteration theory. Prerequisites: CS143 or equivalent
Terms: Win | Units: 3

CS 348C: Computer Graphics: Animation and Simulation

Core mathematics and methods for computer animation and motion simulation. Traditional animation techniques. Physics-based simulation methods for modeling shape and motion: particle systems, constraints, rigid bodies, deformable models, collisions and contact, fluids, and fracture. Animating natural phenomena. Methods for animating virtual characters and crowds. Additional topics selected from data-driven animation methods, realism and perception, animation systems, motion control, real-time and interactive methods, and multi-sensory feedback. Recommended: CS 148 and/or 205A. Prerequisite: linear algebra.
Terms: Win | Units: 3

CS 348I: Computer Graphics in the Era of AI

This course introduces deep learning methods and AI technologies applied to four main areas of Computer Graphics: rendering, geometry, animation, and imaging. We will study a wide range of problems on content creation for images, shapes, and animations, recently advanced by deep learning techniques. For each problem, we will understand its conventional solutions, study the state-of-the-art learning-based approaches, and critically evaluate their results as well as the impacts to researchers and practitioners in Computer Graphics. The topics include differentiable rendering/neural rendering, BRDF estimation, texture synthesis, denoising, procedural modeling, view synthesis, colorization, style transfer, motion synthesis, differentiable physics simulation, and reinforcement learning. Through programming projects and homework, students who successfully complete this course will be able to use neural rendering algorithms for image manipulation, apply neural procedural modeling for shape and scene synthesis, exploit data-driven methods for simulating physical phenomena, and implement policy learning algorithms for creating character animation. Recommended Prerequisites: CS148, CS231N
Terms: Win | Units: 3-4

CS 353: Seminar on Logic & Formal Philosophy (PHIL 391)

Contemporary work. May be repeated a total of three times for credit.
Terms: Aut, Win | Units: 2-4 | Repeatable 3 times (up to 12 units total)
Instructors: Icard, T. (PI)
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