CS 210A: Industry Innovation Lab
Two-quarter project course. Focus is on real-world software development. Corporate partners seed projects with loosely defined challenges from their R&D labs; students innovate to build their own compelling software solutions. Student teams are treated as start-up companies with a budget and a technical advisory board comprised of instructional staff and corporate liaisons. Teams will typically travel to the corporate headquarters of their collaborating partner, meaning some teams will travel internationally. Open loft classroom format such as found in Silicon Valley software companies. Exposure to: current practices in software engineering; techniques for stimulating innovation; significant development experience with creative freedoms; working in groups; real-world software engineering challenges; public presentation of technical work; creating written descriptions of technical work.
Terms: Win
| Units: 3-4
CS 210B: Industry Innovation Lab
Continuation of
CS210A. Focus is on real-world software development. Corporate partners seed projects with loosely defined challenges from their R&D labs; students innovate to build their own compelling software solutions. Student teams are treated as start-up companies with a budget and a technical advisory board comprised of the instructional staff and corporate liaisons. Teams will typically travel to the corporate headquarters of their collaborating partner, meaning some teams will travel internationally. Open loft classroom format such as found in Silicon Valley software companies. Exposure to: current practices in software engineering; techniques for stimulating innovation; significant development experience with creative freedoms; working in groups; real world software engineering challenges; public presentation of technical work; creating written descriptions of technical work.
Terms: Spr
| Units: 3-4
Instructors:
Borenstein, J. (PI)
CS 212: Operating Systems and Systems Programming
Covers key concepts in computer systems through the lens of operatingsystem design and implementation. Topics include threads, scheduling,processes, virtual memory, synchronization, multi-core architectures,memory consistency, hardware atomics, memory allocators, linking, I/O,file systems, and virtual machines. Concepts are reinforced with fourkernel programming projects in the Pintos operating system. This classmay be taken as an accelerated single-class alternative to the
CS111,CS112 sequence; conversely, the class should not be taken by studentswho have already taken CS111 or
CS112.
Terms: Spr
| Units: 3-5
Instructors:
Mazieres, D. (PI)
CS 214: Selected Reading of Computer Science Research
Detailed reading of 5-10 research publications in computer science. For undergraduates, the course is an introduction to advanced foundational concepts within a field as well as an in-depth look at detailed research. For graduate students, the course focuses on historical reading as well as an opportunity to discuss the strengths and weaknesses of the work. Both groups of students discuss historical context, how ideas succeeded or did not and why, and how they manifest in modern technology. The discussion of each piece of work includes a guest lecture by one of its authors.
Last offered: Spring 2022
| Units: 3
CS 217: Hardware Accelerators for Machine Learning (EE 244)
This course explores the design, programming, and performance of modern AI accelerators. It covers architectural techniques, dataflow, tensor processing, memory hierarchies, compilation for accelerators, and emerging trends in AI computing. This course will cover modern AI/ML algorithms such as convolutional neural nets, and Transformer-based models / LLMs. We will consider both training and inference for these models and discuss the impact of parameters such as batch size, precision, sparsity and compression on the accuracy of these models. Students will become familiar with hardware implementation techniques for using parallelism, locality, and low precision to implement the core computational kernels used in ML. Students will develop intuitions to make system-level trade-offs to design energy-efficient accelerators. Students will read recent research papers and complete a final design project.
Terms: Win
| Units: 3-4
Instructors:
Olukotun, O. (PI)
;
Tambe, T. (PI)
;
Cheng, J. (TA)
;
Garg, V. (TA)
;
Li, P. (TA)
;
Pullabhatla Smriti, P. (TA)
CS 218: Information Integrity
How do we decide what information to trust? How do artificial intelligence and online media support or degrade our decisions? What does society need, beyond technology, to make good decisions the norm? Society depends on individuals making sound rational decisions on what information to trust. Those decisions depend on information integrity. The Internet is hardly the first information technology to disrupt society. History documents the mechanics of information integrity through a repeating pattern of new technology exploited by creators and destroyers of reliable information. After establishing basic context, this course will survey technologies that have transformed and disrupted society, from the invention of writing, to the book, the printing press, economical newsprint, the telegraph, and broadcast media on to the Digital Age. Lectures and assignments will explore the harms, real and imagined, of new information technologies. We'll recognize how society adapted, and practice applying these insights to the management of modern information systems.
Last offered: Winter 2025
| Units: 3
CS 220: Researching, Presenting and Publishing Work in AI & Education (EDUC 481)
"Which conference or journal is the right venue for my AI+education paper? How do I get CS folks to care about my education-focused work? How do I explain AI methods to a non-technical audience in education?" These are a few of the most common questions we hear from students seeking to navigate the emergent field at the intersection of AI and education. This seminar provides an interdisciplinary forum for PhD students and advanced masters students from education, computer science, and related fields to support them with the complexities of conducting and disseminating research across disciplinary boundaries. Through collaborative discussions, presentations, and peer feedback, students will gain insights into effectively navigating and bridging technical and educational communities' research and publication norms.
Terms: Spr
| Units: 2-4
Instructors:
Demszky, D. (PI)
CS 221: Artificial Intelligence: Principles and Techniques
Artificial intelligence (AI) has had a huge impact in many areas, including medical diagnosis, speech recognition, robotics, web search, advertising, and scheduling. This course focuses on the foundational concepts that drive these applications. In short, AI is the mathematics of making good decisions given incomplete information (hence the need for probability) and limited computation (hence the need for algorithms). Specific topics include search, constraint satisfaction, game playing,n Markov decision processes, graphical models, machine learning, and logic. Prerequisites:
CS 103 or
CS 103B/X,
CS 106B or
CS 106X,
CS 109, and
CS 161 (algorithms, probability, and object-oriented programming in Python). We highly recommend comfort with these concepts before taking the course, as we will be building on them with little review.
Terms: Aut, Spr
| Units: 3-4
Instructors:
Charikar, M. (PI)
;
Ge, C. (PI)
;
Liang, P. (PI)
;
Yan, S. (PI)
;
Arora, A. (TA)
;
Badlani, A. (TA)
;
Liu, K. (TA)
;
Liu, L. (TA)
;
Mlauzi, I. (TA)
;
Oh, M. (TA)
;
Ramirez, E. (TA)
;
Wu, Z. (TA)
CS 221M: Mechanistic Interpretability
What is the internal structure of modern neural networks and how can we study it? This course provides a broad and deep introduction to interpretability, the subfield of machine learning concerned with understanding precisely how models process information and why they produce the outputs they do. We will cover topics such as probing, steering, causal abstraction, and sparse autoencoders, with a particular emphasis on causal methods and large language models. The course will include guest lectures from leading interpretability labs across academia and industry.
Terms: Spr
| Units: 3
Instructors:
Icard, T. (PI)
CS 222: AI Agents and Simulations
How might we craft simulations of human societies that reflect our lives? Many of the greatest challenges of our time, from encouraging healthy public discourse to designing pandemic responses, and building global cooperation for sustainability, must reckon with the complex nature of our world. The power to simulate hypothetical worlds in which we can ask "what if" counterfactual questions, and paint concrete pictures of how a multiverse of different possibilities might unfold, promises an opportunity to navigate this complexity. This course presents a tour of multiple decades of effort in social, behavioral, and computational sciences to simulate individuals and their societies, starting from foundational literature in agent-based modeling to generative agents that leverage the power of the most advanced generative AI to create high-fidelity simulations. Along the way, students will learn about the opportunities, challenges, and ethical considerations in the field of human behavioral
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How might we craft simulations of human societies that reflect our lives? Many of the greatest challenges of our time, from encouraging healthy public discourse to designing pandemic responses, and building global cooperation for sustainability, must reckon with the complex nature of our world. The power to simulate hypothetical worlds in which we can ask "what if" counterfactual questions, and paint concrete pictures of how a multiverse of different possibilities might unfold, promises an opportunity to navigate this complexity. This course presents a tour of multiple decades of effort in social, behavioral, and computational sciences to simulate individuals and their societies, starting from foundational literature in agent-based modeling to generative agents that leverage the power of the most advanced generative AI to create high-fidelity simulations. Along the way, students will learn about the opportunities, challenges, and ethical considerations in the field of human behavioral simulations. Prerequisites: Team projects and some course assignments will involve programming in Python. Having a background in human-centered design (e.g.,
CS 147,
ME 115A, or a class from the d.school), AI (e.g.,
CS 221,
CS 224), or social psychology may be helpful, although it is not required.
Last offered: Autumn 2024
| Units: 3