Value of Data and AI
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 project in this field.
This course will require 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. However, the course will be accessible to a wide audience including graduate students in computer science, engineering, economics, law and business.
Piazza sign-up link: piazza.com/stanford/winter2020/cs320
Scribes should be forwarded to the TA within one week after each lecture:
The instructors will be available after each class. Please email for additional meetings.
Course project (65%): The main assignment is a quarter-long project, which could range from original research to a case study of a particular company or industry. We will ask students to form small groups and submit a project proposal in the first two weeks of the course, and we will then meet with each group several times to gauge their progress and provide advice. Each group will do an in-class presentation at the end of the course, and possibly a mid-quarter presentation too. Each group will also submit a final report (up to 8 pages).
Class participation (20%): every student should read the assigned papers and actively engage in class discussions.
Class scribing (15%): students will be responsible for scribing one class. Good scribing should supplement the class discussion with additional readings.
Readings (required in red)
|1/8||Introduction, examples of data and business models (Lecture Slides) (Lecture Notes)||
1. AI and economics.
Business value of data, Netflix case study
Design of data platforms, Databricks case study
What can ML do. Statistical methods to evaluate impact.
1. What can ML do.
Data-driven targeting and commerce
Consumer privacy and data security
|1/29||Guest speaker: Hal Varian, Google Chief Economist (Bio & CV): Google Ad Auction History||
Project proposals due
|1/31||Business models and scaling effects|
|2/5||Data valuation; case study|
Guest speaker: Brad Peterson, NASDAQ CIO: NASDAQ Data Products
|2/14||Guest Speaker: Chris Re, Stanford CS, Lattice Data and Apple: data and machine learning||
|2/19||ML accountability and fairness||
1. AI fairness.
|2/21||Guest speaker: David Engstrom, Professor at Stanford Law School: Government agency use of AI and data||
|2/26||Project update presentations.||
|2/28||Regulations around data sharing, data dividend||
|3/4||Guest speaker: Nicole Vadivel, research engineer at Tesla: Fleet field data and battery development||
|3/6||How VCs value data driven startups. Partners from NEA and A16Z.||
|3/11||Challenges for ML production||
|3/13||Wrap up: the future of data and AI||
Will take place during our final exam period 3:30-6:30pm.
The syllabus page shows a table-oriented view of the course schedule, and the basics of course grading. You can add any other comments, notes, or thoughts you have about the course structure, course policies or anything else.
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