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: Win
| Units: 2-4
Instructors:
Demszky, D. (PI)
;
Tan, M. (TA)
CS 294S: Research Project in Software Systems and Security
Topics vary. Focus is on emerging research themes such as programmable open mobile Internet that spans multiple system topics such as human-computer interaction, programming systems, operating systems, networking, and security. May be repeated for credit. Prerequisites:
CS 103 and 107.
Last offered: Autumn 2020
| Units: 3
| Repeatable
for credit
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
Instructors:
Achour, S. (PI)
;
He, Z. (TA)
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.
Last offered: Autumn 2023
| Units: 1
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 309: Industrial Lectureships in Computer Science
Guest computer scientist. By arrangement. May be repeated for credit.
| Units: 1
| Repeatable
for credit
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 315B: Parallel Computing Research Project
Advanced topics and new paradigms in parallel computing including parallel algorithms, programming languages, runtime environments, library debugging/tuning tools, and scalable architectures. Research project. Prerequisite: consent of instructor.
Last offered: Autumn 2022
| Units: 3
CS 320: Value of Data and AI
Many of the most valuable companies in the world and the most innovative startups have business models based on data and AI, but our understanding about the economic value of data, networks and algorithmic assets remains at an early stage. For example, what is the value of a new dataset or an improved algorithm? How should investors value a data-centric business such as Netflix, Uber, Google, or Facebook? And what business models can best leverage data and algorithmic assets in settings as diverse as e-commerce, manufacturing, biotech and humanitarian organizations? In this graduate seminar, we will investigate these questions by studying recent research on these topics and by hosting in-depth discussions with experts from industry and academia. Key topics will include value of data quantity and quality in statistics and AI, business models around data, networks, scaling effects, economic theory around data, and emerging data protection regulations. Students will also conduct a group research projects in this field.nnPrerequisites: Sufficient mathematical maturity to follow the technical content; some familiarity with data mining and machine learning and at least an undergraduate course in statistics are recommended.
Last offered: Winter 2022
| Units: 3