231 - 240 of 366 results for: CS

Leveraging off three synchronized sets of symbolic data resources for notation and analysis, the lab portion introduces students to the open-source Humdrum Toolkit for music representation and analysis. Issues of data content and quality as well as methods of information retrieval, visualization, and summarization are considered in class. Grading based primarily on student projects. Prerequisite: 253 or consent of instructor.

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

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.

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.

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.

Course focuses on how to build modern computing systems, namely notebooks, smartphones, and data centers, covering primarily their hardware architecture and certain system software aspects. For each system class, we cover the system architecture, processor technology, advanced memory hierarchy and I/O organization, power and energy management, and reliability. We will also cover topics such as interactions with system software, virtualization, solid state storage, and security. The programming assignments allow students to explore performance/energy tradeoffs when using heterogeneous hardware resources on smartphone devices.

The regulation of artificial intelligence may present the most pressing policy issue of our time. AI represents more than just a technology or tool; it promises to revolutionize the economy and all social systems. Governments around the world are struggling to keep up with the fast pace of AI development and to adapt existing regulatory regimes to these dramatic changes. This course surveys current and emerging legal, policy and governance challenges related to artificial intelligence. The course will explore regulatory initiatives and proposals from around the world, such as the European AI Act and U.S. Executive Orders, while also examining how existing laws related to privacy, data protection, intellectual property, civil rights, and national security apply to this developing technology. The course will also cover the AI policy debates related to balancing innovation and safety in a variety of contexts, from autonomous vehicles and weapons, to social media and elections. Cross-cutting themes will include: how law and policy affect the way important societal decisions are justified; the balance of power and responsibility between humans and machines in different settings; the incorporation of multiple values into AI decision-making frameworks; the interplay of norms and formal law; technical complexities that may arise as society scales deployment of AI systems; AI's implications for transnational law and governance and geopolitics; and similarities and differences to other domains of human activity raising regulatory trade-offs and affected by technological change. Note: The course is designed both for students who want a survey of the field and lack any technical knowledge, as well as students who are AI experts but wish to learn more about the relevant policy questions and law. Technical knowledge or familiarity with AI is not a prerequisite. Requirements: The course involves a considerable amount of reading plus active classroom discussion. Elements used in grading: Requirements include attendance, class participation and a research paper. After the term begins, students accepted into the course can transfer, with consent of the instructor, from section (01) into section (02), which meets the R requirement. Research Paper of roughly 25 pages.

This course in artificial intelligence will provide a deep dive into advanced computer vision techniques for reasoning about visual data, and their real-world use in biomedicine. We will cover current cutting-edge models including different families of vision foundation models from representation learners to diffusion and generative models, and both vision-only and vision-language models. We will also cover considerations for real-world use, including model size and computation, training and inference settings, and training data, focusing on applications in biomedicine. Students will be actively engaged in studying and analyzing recent advancements through written analyses, class discussions, and a final project. This course is considered an advanced course and students should be comfortable with deep learning and computer vision at the level of CS231N or BIODS220.

This course explores how healthcare data is generated in practice - and how these processes influence the development of robust, trustworthy machine learning models. Students will examine how clinical workflows, documentation habits, and institutional practices introduce bias, noise, missingness, and spurious correlations into datasets. The course emphasizes the often-overlooked reality that many model failures stem not from algorithmic design, but from subtle flaws in how data is collected, labeled, and interpreted. Through lectures, real-world case studies, and observation of clinical data workflows, students will develop practical tools for understanding and improving data quality and model reliability. Topics include assessing data quality, addressing missingness and label leakage, developing scalable and accurate annotation strategies, leveraging synthetic data, and monitoring data and models for drift over time. Students will also learn to evaluate how these data challenges impact model generalization and robustness.