Reid Hoffman Shares Lessons (Links to an external site.).WeWork: Blitzscaling or Blitzflailing? (Links to an external site.).
ML accountability and fairness ( Lecture Note)īusiness Models & Scaling Effects ( Lecture Note)
Robust De-anonymization of Large Sparse Datasets. California data brokers registry (from CCPA). Guest Speaker: David Engstrom (Stanford Law School)( Lecture Note) Hidden Technical Debt in Machine Learning Systems. Rohit Nishant et al., Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda #Ai project canvas how to#
How to build an analytics team for impact in an organizationĭata and AI for energy and sustainability ( Lecture slides, Lecture Note)Ģ. Evolution of decision support systems (up to page 18) (Ch.1 of Building the Data Warehouse). Guest speaker: Susan Athey (Stanford GSB), "The Value of Data for Personalization" ( Lecture slides, Lecture Note)ĭesign of data platforms, Databricks case study Guest Talk on NASDAQ Data Products: Brad Peterson (NASDAQ CTO/CIO), Bill Dague (Head of Alternative Data) and Mike O'Rourke (SVP, Head of AI)Ĭase studies of ML applications and ways to evaluate ML impact ( Lecture Note) Introduction, examples of data and business modelsīusiness value of data, Netflix case study Good scribing should supplement the class discussion with additional readings. Each group will also submit a final report (up to 8 pages).Ĭlass participation (20%): every student should read the assigned papers and actively engage in class discussions.Ĭlass scribing (15%): students will be responsible for scribing one class. Each group will do interim and final presentations. 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 to gauge their progress and provide advice. AssignmentsĬourse 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. Please email the instructors if you'd like to meet. The instructors are all available to meet with you. Scribed notes Word template Links to an external site. Scribed notes LaTex template Links to an external site. Scribes' sign-up sheet (Links to an external site.). Scribes should be forwarded to the TA within one week after each lecture: Piazza sign-up link: /stanford/winter2021/cs320 (Links to an external site.) Lingjiao Chen (Links to an external site.) ( ) Matei Zaharia Links to an external site. ( Zou (Links to an external site.) ( Eglash Links to an external site. ( TA 
However, the course will be accessible to a wide audience including graduate students in computer science, engineering, economics, law and business. 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. This course is zoom only and there will not be any in-person meeting. Location: Wednesdays and Fridays at 10-11:20, via Zoom. Students will also conduct a hands-on research project in group. We will have guest speakers from NASDAQ, Facebook, etc to talk about industrial view of data and AI. Key topics will include value of data quantity and quality in statistics and AI, AI service marketplaces, business models, social good and justice around data, economic theory, regulation around data, and emerging data protection regulations. For example, what is the value of a new dataset or an improved algorithm? How should practitioners understand and exploit the AI service market to obtain accurate and cheap predictions? 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. 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.
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