Assignments

Due dates

• All assignments will be due on Wednesdays at the start of class.

• Assignments will be posted on this web page one week before they are due.

Marking scheme and lateness penalties

• Each of the 3 assignments will be worth 15% of the final grade.

• Except in the case of an official Student Medical Certificate, assignments that are submitted late will have their score reduced by 25% per day or part of a day (so after 4 days they will get zero however good they are). The time past the deadline will be rounded UP to an integer number of days.

Collaboration Policy for Assignments

• You are expected to work on the assignments by yourself. You should not discuss them with anyone except the tutors or the instructor. The report you hand in should be entirely your own work.
• Assignment 1
  assoc.m
  This involves answering some questions about generalization, understanding a short matlab program, and implementing the backpropagation algorithm to learn to recognize hand-written digits.
  

• Assignment 2
  You will need to download the following matlab files to use the modified version of minimize.m. Do not use the version available from Rasmussen's webpage.
  minimize.m
  checkgrad.m
  ECG.m
  runme.m
  toydata.mat
  toydata_test.mat
  Files ECG.m and runme.m demonstrate how minimize.m can be used to train a neural network with 3 hidden layers. runme.m is the driver routine that alternates between calling minimize.m and computing training and validation errors. ECG.m is the function passed to minimize.m that computes the gradients and outputs for the neural network. Note that since the mixture-fitting code you need to write will be much simpler than the code in ECG.m and runme.m, your time might be better spent reading the documentation for minimize.m rather than trying to understand ECG.m and runme.m in minute detail.

• Assignment 3
  Many people found assignment 3 difficult. I am therefore making it faster to do the empirical part by reducing the amount of training data and I will explain the learning procedure for RBM's in much more detail in the lecture on Nov 5. The deadline for assignment 3 is now wed Nov 12 at 1.00pm. Please tell anyone else you know in the class. The reduced training data will be just the first two mini-batches of the current training data so there will only be 20 examples of each of the 5 digit classes.
  If you have already finished assignment 3 you are welcome to hand in it in as is.
  Andriy Mnih will be grading assignment 3. He will have his usual office hours from 3.00 to 4.00 on thursday nov 6, but he will NOT have office hours on monday Nov 10.