[Research] new research seminar in machine learning and policy

[Daniel B. Neill]

Dear Auton students,

Perhaps some of you might be interested in a new course that I'll be 
teaching in the spring (mini-4).  I've attached a course description 
below.  Please feel free to forward to any other student groups that might 
be interested; I've already sent out announcements to MLD and Heinz.

Best,
Daniel

--------------------------------------------------------------------
90-904, Research Seminar in Machine Learning and Policy
Instructor: Daniel B. Neill (neill at cs.cmu.edu)
6 credits, Mini-4, Mon/Wed 10:30-11:50am

The Research Seminar in Machine Learning and Policy (90-904) is a brand-new, 
Ph.D. level course intended for Heinz and MLD students 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.

This course is meant to provide in-depth coverage of selected topics in machine 
learning and policy.  While the set of discussion topics will vary from 
semester to semester, examples include:

1. Which policy problems are, and are not, amenable to application of machine 
learning techniques?
2. How can we integrate machine learning methodologies with core policy methods 
from econometrics, statistics, management science, and organizational behavior?
3. How can we use prediction methods (classification and regression) to inform 
policy decisions?
4. How can we use Bayesian networks and other graphical models to understand 
the relationships between variables in policy domains?
5. 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?
6. 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?
7. How can we channel the "wisdom of crowds" into productive tasks (e.g. games 
with a purpose, prediction markets).
8. How can we make machine learning systems valuable to users in real-world 
application domains?

This course has been developed as part of a joint Heinz/MLD initiative to build 
closer ties between researchers and students in machine learning and policy. 
Another aspect of this initiative is the establishment of a new Joint Ph.D. 
program in Machine Learning and Public Policy 
(http://www.ml.cmu.edu/prospective_students/ml_heinz_phd.html). Please feel 
free to contact me (neill at cs.cmu.edu) if you would like more information about 
these courses or are interested in learning more about the joint program.