Reading: Chapter 4 of "Machine Learning" by Tom M. Mitchell. Note, Sections 4.5.3 and 4.8 are optional. However, section 4.8 may provide ideas for the Part II of this assignment.

Your assignment: Follow the instructions for Mitchell and Blum's "Neural Networks and face images" assignment. The first level of detail for this project is described in section 4.7 of Mitchell's text. The remaining details for the NN implementation and assignment are in hw96.ps, attached if you got this handout in class. You are provided with a NN implementation in C, which you will have to modify a little. Note that this assignment description says "Homework 5", but this is actually your second assignment, and also note the above due date, in contrast to that in the description.

Like the previous homework, this assignment has two parts, the second of which is open-ended. For this assignment, we strongly recommend that you team up for the second part. For the second part you will hand in a write-up.

Part I:

Changes from the assignment description file:

• Absolute pathnames should be changed according to Jeff's instructions (TBA).
• Remote students with no AcIS account should arrange with Jeff to get the data.

For Part I you are to hand in answers to the questions listed among the 13 steps described in the assignment.

Part II:

For the open-ended portion of the assignment, we strongly recommend you team up with one or two other people. This way, you can build on each other's ideas, share the implementation and write-up responsibilities, and have more fun. For this portion, you hand in one write-up for your group.

Since you do not have to write the implementation from scratch, emphasis is weighted more on Part II. However, do be careful again to keep your experiments very reasonable in terms of effort. If you have "big ideas", they may best wait until the final project, which is, in principle, completely open-ended (i.e., can use machine learning, and NN's in particular).

Please choose something that you find interesting. Your choice should be motivated, not just random. You should be able to explain why your experiment was an interesting thing to try out.

In addition to the suggestions listed in the assignment description file, other possibilities include:

• Apply the package to your own data, i.e., image data or otherwise. For example, cameras in the SRL lab will allow you to take your own pictures of faces or other objects. However, you will need help from someone with a computer science account. If you collect this data, you may want to continue using it for your final project. fyi, a CRF person said: The software is in /opt/SUNWits and /opt/SUNWrtvc. I think you can use "SunVideo", "rtvc_display", "rtvc_capture_movie"
• One student at CMU reportedly created a "sunglass learner/generator" that placed sunglasses on a sunglasses-less face.

Deliverables for Part II:

• a written description of the experiments you conducted, 4 page max. This should describe the details of your modification, the motivation ("why?"), and the resulting change of performance. As supporting reference material for this, also append:
  • hard copy of additional or modified code (well documented). Please organize your code well.
  • hard copy of sample runs (also well documented)
• a slide on what you did and the results. I will "flash" these slides for the class -- the purpose here is so everyone get's a sense of what everyone else did. You can spend 10 minutes with a magic marker to do this and get full credit, or you can make it fancier if you like.
• (an electronic submission of your code, or a brief demo, may also be requested later)

email: evs at cs dot columbia dot edu