[Research] spring courses in Machine Learning and Policy

[Daniel B. Neill]

Dear Heinz and SCS students, I wanted to advertise two half-semester 
yresearch seminars that I will be teaching this spring, at the 
intersection of Machine Learning and Public Policy.  Hope to see you 
there!

Best regards,
Daniel

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Daniel B. Neill
Assistant Professor of Information Systems
Carnegie Mellon University
neill at cs.cmu.edu


90-904/10-830, Research Seminar in Machine Learning and Policy
Mini-3, Mon/Wed 10:30-11:50am

This research seminar is intended for Ph.D. students in Heinz College, the 
Machine Learning Department, and other university departments who wish to 
engage in cutting-edge research at the intersection of machine learning 
and public policy.  Qualified master's students may also enroll with 
permission of the instructor; all students are expected to have some prior 
background in machine learning and/or artificial intelligence (10-601, 
10-701, 90-866, or a similar course).

The course has three main objectives: 1) to facilitate in-depth 
discussions of current research articles and essential topics in machine 
learning and policy, 2) to benefit the students' own ongoing research 
projects through presentations, critiques, and discussions, and 3) to 
encourage interdisciplinary research collaborations between students in 
Heinz, MLD, and other departments.  We plan to achieve these goals through 
a discussion-based course format: students will present and discuss 
current research articles on selected topics in machine learning and 
policy, as well as giving presentations on their ongoing research projects 
and/or smaller-scale course projects in this domain. While the set of 
discussion topics will vary from semester to semester, example topics from 
previous semesters include:

* New Directions in Supervised Learning
* Data Visualization and Exploratory Analysis
* Social Network Analysis
* Web Mining, Internet Search, and Information Retrieval
* Harnessing the Wisdom of Crowds
* Graphical Models and Causality
* Machine Learning for Decision Support
* Data Mining and Privacy
* Event and Pattern Detection
* Data from Emerging Technologies

Some of the many questions we may address include the following:

* Which policy problems are, and are not, amenable to application of
   machine learning techniques?
* How can we integrate machine learning methodologies with core policy
   methods from econometrics, statistics, management science, and
   organizational behavior?
* How can we combine machine learning and operations research
   methodologies to find patterns in massive datasets?  How can such data
   mining techniques be used for the public good without violating
   individual privacy?
* How can we integrate approaches to modeling and mining of social network
   data with a policy-based understanding of the formation and evolution of
   social ties?
* How can we channel the "wisdom of crowds" into productive tasks?
* How can we make machine learning systems valuable to users in real-world
   application domains?

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90-921/10-831
Special Topics in Machine Learning and Policy: ML for the Developing World
Mini-4, Tues/Thurs 1:30-2:50pm

This research seminar is intended for Ph.D. students in Heinz College, the 
Machine Learning Department, and other university departments who wish to 
engage in detailed exploration of a specific topic at the intersection of 
machine learning and public policy.  Qualified master's students may also 
enroll with permission of the instructor;  all students are expected to 
have some prior background in machine learning and/or artificial 
intelligence (10-601, 10-701, 90-866, 90-904/10-830, or a similar course).

This year's course will focus on the topic of Machine Learning for the 
Developing World.  We will explore the potential contributions of machine 
learning technologies for development, including potential impacts on 
healthcare, education, agriculture, finance, communications, and 
governance. Machine learning can be used to analyze existing data and 
assist with the targeted collection of new data to drive policy analysis, 
and can be incorporated into deployed information systems to improve the 
effectiveness of public services.  However, application of machine 
learning to the developing world faces a number of challenges (e.g. 
sparsity and low quality of data) as well as many opportunities for the 
development of new methods and incorporation of new data sources.  We will 
explore these challenges and opportunities in detail through lectures, 
discussions on current research articles and future directions, and course 
projects, with the goals of understanding and advancing the current state 
of the art.