Unless otherwise specified, questions have unit value. The total value of the assignments from each week will vary substantively. Recall that assignments are graded rather loosely on effort, and that 3/4 of the total marks (1/2 for ugrads) over all assignments over all weeks represents 100%. This policy is in place partly to allow for error in the grading approach which, by necessity, is somewhat subjective, and needs to be done somewhat superficially. It is recommended (and requested) that you try to overshoot the 3/4 requirement, rather than worry about the details of how the grading is done. Problems denoted EXTRA can be substituted for other problems, or done in addition, but they do not count towards the computation of the 3/4 requirement. They may be discussed in class depending on time and interest. They are problems that I think might be useful, and likely be assigned if we had more time per chapter. Sometimes you will explicitly have to choose some of your own problems. Even when this is not the case, you can substitute some problems in the book if they appear more helpful to you. For now, limit the number of substitutions to 50% of what you hand in. This parameter may be increased or decreased as we go on. You are encouraged to discuss the problems with your peers, but I would like individual final submissions demonstrating effort and understanding of what was done. If you end up working closely with someone on a problem set, make a note on your submission saying who it was. Since this is graduate level research course that is graded predominately on effort, I am confident that there will not be any problems with academic honesty. However, do note that non-negligible deviations are often surprisingly easy to spot, and can be verified by discussing the submitted solutions with the student. ----------------------------------------------------------------------------- Problems for Week 3, due Thursday, Sep 18. Total value is 8. ----------------------------------------------------------------------------- 1. Problem 8.3 2. Problem 8.6 3. Problem 8.10 4. Problem 8.13 QUADRUPLE VALUE 5. The data files face_train.txt no_face_train.txt face_test.txt no_face_test.txt are made from images of faces and non-faces as follows. The images were converted to black and white and divided into a 7 by 7 grid, and each block was averaged to produce 49 numbers for each image, which are recorded in the rows of the above files. This is clearly not a very intelligent way to extract features for face detection, but suffices for experimentation. Build a Naive-Bayes classifier from the data on the assumption that the conditional densities are Gaussian. Write a few sentences about what you did, and what your accuracy was on the training and test data sets. Further, comment on some of the assumptions this classifier relies on. For the record, submit your program that computes the answer, but the graders won't necessarily look at it unless something your writeup suggests that they should. Hence you can use any programming language that you like. Again, if you have no opinion, I suggest using Matlab. If you want to try Matlab, but have no prior experience, then you will have to spend some time getting familiar with it, which is likely time well spent. Some information about getting started is linked here.