CS 281, Spring 1998, Machine Learning
Term Project Proposals, due 4/1. Projects due 5/15

You should write up a one-page proposal for a project by April 1, and be ready to discuss it with me to make sure it's interesting and feasible. Many possible projects will involve material you have not yet read. You will need to read ahead now in order to write a proposal in areas such as neural nets, ILP, etc.

Final project reports will be due May 15. There will be a session or two of short project presentations the same day. Projects may be done singly or in pairs.

Practical projects

• Version-space learning---empirical application. Try, for example, to come up with predictive rules for building use given structural descriptions of actual buildings (shops, restaurants, professional offices, banks, office buildings, gas stations, houses of ill repute, academic buildings, municipal buildings, supermarkets, homes) around Berkeley. Or maybe try the NetTalk data.
• Version-space learning---analytic application. LEX (Mitchell, Utgoff, and Banerji, 1983) uses version spaces to solve symbolic integration problems. Do the same for solving algebraic equations (eg solve $x^2 - 3x+2 = cosh xy$ for $x$).
• Build an incremental decision-tree program: constructs the best DT it can from the data so far, and updates it as new data comes in without having to remember or process all previous examples (additional readings available).
• Apply reinforcement learning with a neural network function approximator to develop an effective evaluation function for a probabilistic game such as backgammon (additional readings and code available).
• Implement boosting as a general wrapper method that can use an abitrary learning algorithm as a subroutine; compare the performance of the boosted and unboosted version on some Irvine data sets.
• Learn to use the Progol system for ILP, applying it to as many domains as possible (code available).
• Empirically investigate the time and example complexity of support vector machines, comparing them to decision trees and/or theoretical predictions.
• Investigate empirically various possible parameter optimization methods for neural networks, including ``natural gradients'' (readings available).
• Implement a Bayesian version of neural network learning (or other regression method) using a distribution over parameter values, as described in Chapter 10 of Bishop.
• Implement an APN system on top of an existing probabilistic inference system and apply it to (e.g.) the insurance data; extend your system to handle continuous variables.
• Implement a structural learning algorithm for belief nets (Cooper and Herskovits, Heckerman et al, or Friedman) and apply it to (e.g.) the insurance data.
• Write a program that searches the space of logical concepts in elementary number theory, generating and building on interesting ones and finding conjectures relating them.
• Build a simple reinforcement learning system for a robot motion planning problem or for a simulated environment such as the Wumpus world (code available).
• Investigate experimentally the benefits of applying subset selection recursively as the decision tree is constructed.
• Implement and test one or more methods for in-context spelling correction.

Theoretical projects

• Survey what is known about the PAC-learning of probabilistic networks from data, delineate the remaining open problems, and discuss possible avenues for their solution.
• Investigate the state of the art in both theory and practice for nearest neighbour methods in very high dimensions (e.g., images). Is this a practical approach to learning from image data? Discuss how the problems might be overcome, with reference to specific successful systems.
• Survey what is known theoretically about online learning of linear predictors and mixtures-of-experts, including the ``exponentiated gradient'' methods. Can these algorithms be viewed as a form of Bayesian learning? If so, make a precise translation for at least one such algorithm.
• Look into the SOAR literature, providing an understandable description of the principles, design and operation of the system. Make a detailed comparison between chunking (SOAR's learning mechanism) and EBL (additional readings available).
• Discuss possible definitions of completeness for ILP systems. Survey currently known results on completeness, and delineate the remaining open problems.
• Consider a simple environment--agent pair, such as the Wumpus world. Find a sequence of theories beginning at the level of the sensory inputs and culminating in something approximating to a correct model of the environment. Each theory should partially explain more of the sensory observations, and include new predicates that succeed in shortening the overall representation. For extra credit, show this is possible in general (ie the search space is unimodal) or give a counterexample.