CMU 10-806 Foundations of Machine Learning and Data Science, Fall 2015

Ideas for Projects

One of the course requirements is to do a project, which you may do individually or in a group of 2 to 3. Here are a few ideas for possible topics for projects. You might also want to take a look at recent COLT, ICML, or NIPS proceedings. All the recent COLT proceedings contain a few open problems, some with monetary rewards!

Project Ideas

Noise tolerant computationally efficient algorithms: Machine learning lenses in other areas: Distributed machine learning: Semi-supervised learning and related topics: Interactive learning: VC-dimension and other methods of obtaining sample size bounds: Online learning: Clustering and related topics: Hardness of learning: Multiclass classification: Relationship between convex cost functions and discrete loss: These papers look at relationships between different kinds of objective functions for learning problems. Boosting related topics: Learning with kernel functions: Learning in Markov Decision Processes: See M. Kearns's home page and Y. Mansour's home page for a number of good papers. Also S. Kakade's thesis.

Learning in Graphical Models (Bayes Nets)