COMPSCI 126 - Fairness and Privacy: Perspectives of Law and Probability or return to Course Catalog Search
204972 – Section 001
|Faculty of Arts and Sciences||Computer Science||Cynthia Dwork and Martha Minow|
|Term||Day and Time||Location|
|Fall 2019-2020 (show academic calendar)||MW Monday 9:00 a.m. - 10:10 a.m.; Wednesday 9:00 a.m. - 10:15 a.m.||Maxwell Dworkin 221 (SEAS)|
Wasserstein 3007 (HLS)
4 (show credit conversion for other schools)
Credit in Faculty of Arts and Sciences is equivalent to:
Students will learn to analyze and mitigate privacy loss, unfairness, and lack of statistical validity, in data analysis. Principal techniques will come from cryptography, differential privacy, and the newly emerging areas of adaptive data analysis and fairness in machine learning.
Please note that we are still reviewing applications for the CS portion of this course, and final acceptance decisions may come later in shopping week. CS Applicants should submit letters of inquiry with CVs to Allison Choat, firstname.lastname@example.org , by midnight, Wednesday, September 4. All applicants should plan to attend an opening lecture dinner for the class at 7:00 PM, Tuesday, Sept. 3rd, on HLS campus (room TBD). To attend the dinner, e-mail Allison Choat at email@example.com prior to Thursday, August 29, at 3:30 PM. Whether or not you can attend the dinner, if you have not been informed of your application status, please plan to attend the first formal CS-specific class on Wednesday, September 4th, 9:00-10:15 AM, in Maxwell-Dworkin 221. Course enrollment limited. Offered jointly by HLS and SEAS, with interwoven tracks emphasizing, respectively, law and computer science, the tracks will meet jointly and separately. Admission is by permission of instructors; applicants should submit letters of inquiry with CVs to Rachel Keeler, firstname.lastname@example.org, by August 25, 2019.
|Eligible for cross-registration|
With permission of instructor/subject to availability
MIT students please cross register from MIT's Add/Drop application.