printer friendly pageCS 112: Operating systems kernel implementation project
Students will learn the details of how operating systems work throughfour implementation projects in the Pintos operating system. Theprojects center around threads, processes, virtual memory, and filesystems. This class should not be taken by students who have taken orplan to take CS212 or
CS140. Prerequisite: CS111 or permission of theinstructor.
Terms: Win
| Units: 3
Instructors:
Mazieres, D. (PI)
;
Baruah, N. (TA)
;
Bhak, N. (TA)
;
DeMarco, D. (TA)
;
Martinez-Piedra, G. (TA)
;
Nambi, S. (TA)
;
Park, J. (TA)
;
Yu, S. (TA)
CS 114: 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
CS 120: Introduction to AI Safety (STS 10)
As we delegate more to artificial intelligence (AI) and integrate AI more in societal decision-making processes, we must find answers to how we can ensure AI systems are safe, follow ethical principles, and align with the creator's intent. Increasingly, many AI experts across academia and industry believe there is an urgent need for both technical and societal progress across AI alignment, ethics, and governance to understand and mitigate risks from increasingly capable AI systems and ensure that their contributions benefit society as a whole. Intro to AI Safety explores these questions in lectures with targeted readings, weekly quizzes, and group discussions. We are looking at the capabilities and limitations of current and future AI systems to understand why it is hard to ensure the reliability of existing AI systems. We will cover ongoing research efforts that tackle these questions, ranging from studies in reinforcement learning and computer vision to natural language processing. W
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As we delegate more to artificial intelligence (AI) and integrate AI more in societal decision-making processes, we must find answers to how we can ensure AI systems are safe, follow ethical principles, and align with the creator's intent. Increasingly, many AI experts across academia and industry believe there is an urgent need for both technical and societal progress across AI alignment, ethics, and governance to understand and mitigate risks from increasingly capable AI systems and ensure that their contributions benefit society as a whole. Intro to AI Safety explores these questions in lectures with targeted readings, weekly quizzes, and group discussions. We are looking at the capabilities and limitations of current and future AI systems to understand why it is hard to ensure the reliability of existing AI systems. We will cover ongoing research efforts that tackle these questions, ranging from studies in reinforcement learning and computer vision to natural language processing. We will study work in interpretability, robustness, and governance of AI systems - to name a few. Basic knowledge about machine learning helps but is not required. View the full syllabus at
http://tinyurl.com/42rb2sfv. Enrollment is by application only. Apply online at
https://forms.gle/v8msM8nJ5FgeEHx1A by 9:00 PM PDT on Saturday, March 16, 2024.
Terms: Spr
| Units: 3
Instructors:
Lamparth, M. (PI)
;
Hardy, A. (TA)
CS 123: A Hands-On Introduction to Building AI-Enabled Robots
This course offers a hands-on introduction to AI-powered robotics. Unlike most introductory robotics courses, students will learn essential robotics concepts by constructing a quadruped robot from scratch and training it to perform real-world tasks. The course covers a broad range of topics critical to robot learning, including motor control, forward and inverse kinematics, system identification, simulation, and reinforcement learning. Through weekly labs, students will construct a pair of tele-operated robot arms with haptic feedback, program a robot arm to learn self-movement, and ultimately create and program an agile robot quadruped named Pupper. In the final four weeks, students will undertake an open-ended project using Pupper as a platform, such as instructing it to walk using reinforcement learning, developing a vision system to allow Pupper to play fetch, or redesigning the hardware to enhance the robot's agility. Note: CS123 strives to achieve a balanced distribution of senio
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This course offers a hands-on introduction to AI-powered robotics. Unlike most introductory robotics courses, students will learn essential robotics concepts by constructing a quadruped robot from scratch and training it to perform real-world tasks. The course covers a broad range of topics critical to robot learning, including motor control, forward and inverse kinematics, system identification, simulation, and reinforcement learning. Through weekly labs, students will construct a pair of tele-operated robot arms with haptic feedback, program a robot arm to learn self-movement, and ultimately create and program an agile robot quadruped named Pupper. In the final four weeks, students will undertake an open-ended project using Pupper as a platform, such as instructing it to walk using reinforcement learning, developing a vision system to allow Pupper to play fetch, or redesigning the hardware to enhance the robot's agility. Note: CS123 strives to achieve a balanced distribution of seniority across the undergrad student body. Within each seniority group, enrollment of students will follow a first-come-first-served approach. Please use the form below to enroll in the class. The form will be open on 9/1/2023 9:00AM Pacific Time. Please use this form to apply:
https://docs.google.com/forms/d/e/1FAIpQLSdBSUqLjpD-a-GmwhPnRLMi7L1BMMzikl8yqwmQp-stMoDqIg/viewform
Terms: Aut
| Units: 3
Instructors:
Liu, K. (PI)
;
Levine, G. (TA)
CS 124: From Languages to Information (LINGUIST 180, LINGUIST 280)
Extracting meaning, information, and structure from human language text, speech, web pages, social networks. Introducing methods (regex, edit distance, naive Bayes, logistic regression, neural embeddings, inverted indices, collaborative filtering, PageRank), applications (chatbots, sentiment analysis, information retrieval, question answering, text classification, social networks, recommender systems), and ethical issues in both. Prerequisites:
CS106B, Python (at the level of
CS106A),
CS109 (or equivalent background in probability), and programming maturity and knowledge of UNIX equivalent to
CS107 (or taking CS107 or CS1U concurrently).
Terms: Win
| Units: 3-4
| UG Reqs: WAY-AQR
CS 125: Data: Algorithms, Tools, Policy, and Society (POLISCI 156)
A broad multidisciplinary examination of the use and impacts of data, including fundamental principles and algorithms, tools for data analysis, visualization, and machine learning, policy issues, and societal considerations. Specific topics include: data provenance (where data comes from and how it's processed), the role and value of data in analytics and decision-making, data and algorithmic fairness, data privacy, the concentration of data as power, and issues of data governance and regulation, including transparency and due process. In addition to case studies, conceptual frameworks, theoretical underpinnings, and algorithms, the course provides practical experience through hands-on work where students use tools to explore issues from class on real data.
CS 129: Applied Machine Learning
(Previously numbered
CS 229A.) You will learn to implement and apply machine learning algorithms. This course emphasizes practical skills, and focuses on giving you skills to make these algorithms work. You will learn about commonly used learning techniques including supervised learning algorithms (logistic regression, linear regression, SVM, neural networks/deep learning), unsupervised learning algorithms (k-means), as well as learn about specific applications such as anomaly detection and building recommender systems. This class is taught in the flipped-classroom format. You will watch videos and complete in-depth programming assignments and online quizzes at home, then come to class for discussion sections. This class will culminate in an open-ended final project, which the teaching team will help you on. Prerequisites: Programming at the level of CS106B or 106X, and basic linear algebra such as
Math 51.
Terms: Win
| Units: 3-4
Instructors:
Bensouda Mourri, Y. (PI)
;
Ng, A. (PI)
;
Doby, S. (TA)
...
more instructors for CS 129 »
Instructors:
Bensouda Mourri, Y. (PI)
;
Ng, A. (PI)
;
Doby, S. (TA)
;
Kaputa, Z. (TA)
;
Marklund, H. (TA)
CS 129X: Human Centered NLP (CS 329X)
Recent advances in natural language processing (NLP), especially around large pretrained models, have enabled extensive successful applications. However, there are growing concerns about the negative aspects of NLP systems, such as biases and a lack of input from users. This course gives an overview of human-centered techniques and applications for NLP, ranging from human-centered design thinking to human-in-the-loop algorithms, fairness, and accessibility. Along the way, we will cover machine-learning techniques which are especially relevant to NLP and to human experiences. Prerequisite: CS224N or
CS224U, or equivalent background in natural language processing. Prerequisite: CS224N or
CS224U, or equivalent background in natural language processing.
CS 131: Computer Vision: Foundations and Applications
Computer Vision technologies are transforming automotive, healthcare, manufacturing, agriculture and many other sections. Today, household robots can navigate spaces and perform duties, search engines can index billions of images and videos, algorithms can diagnose medical images for diseases, and smart cars can see and drive safely. Lying in the heart of these modern AI applications are computer vision technologies that can perceive, understand, and reconstruct the complex visual world. This course is designed for students who are interested in learning about the fundamental principles and important applications of Computer Vision. This course will introduce a number of fundamental concepts in image processing and expose students to a number of real-world applications. It will guide students through a series of projects to implement cutting-edge algorithms. There will be optional discussion sections on Fridays. Prerequisites: Students should be familiar with Python, Calculus & Linear Algebra.
Terms: Win
| Units: 3-4
Instructors:
Gaidon, A. (PI)
;
Niebles Duque, J. (PI)
;
Chang, J. (TA)
...
more instructors for CS 131 »
Instructors:
Gaidon, A. (PI)
;
Niebles Duque, J. (PI)
;
Chang, J. (TA)
;
Chen, H. (TA)
;
Chou, A. (TA)
;
Moore, A. (TA)
;
Qu, C. (TA)
;
Tchapmi, M. (TA)
CS 137A: Principles of Robot Autonomy I (AA 174A, EE 160A)
Basic principles for endowing mobile autonomous robots with perception, planning, and decision-making capabilities. Algorithmic approaches for robot perception, localization, and simultaneous localization and mapping; control of non-linear systems, learning-based control, and robot motion planning; introduction to methodologies for reasoning under uncertainty, e.g., (partially observable) Markov decision processes. Extensive use of the Robot Operating System (ROS) for demonstrations and hands-on activities. Prerequisites:
CS 106A or equivalent,
CME 100 or equivalent (for linear algebra), and
CME 106 or equivalent (for probability theory).
Terms: Aut
| Units: 3-4
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