niielXX.cs.unm.edu
where
XX is a two-digit number between 01 and 20.
/usr/local/pkg/ece547
to your path. We've put
the MNIST database and all of the helper code there already.
Everyone in the class will do a final project (typically in teams of two). Neural networks are my research area, and I am very happy to help students who become particularly excited about some topic learn more about it.
Course materials including assignments will be posted on the web. (You can get extra credit by writing up html lecture notes in a timely fashion for me to post on the web.)
I'm not a stickler about formal prereqs - effort can make up for a lot. But I expect you to not be scared of working through equations when they come up, or thinking in mathematical terms. Let's say ``mathematical training or appropriate mathematical maturity.''
The book is much more signal-processing oriented than this course. You are not obligated to understand material unrelated to the lectures.
Tue | Aug 25 | historical background |
Thu | Aug 27 | my idiosyncratic view of neural networks (ch 2) |
Tue | Sep 1 | perceptrons (ch 3) assignment |
Thu | Sep 3 | in-class demo of perceptron learning |
Tue | Sep 8 | perceptrons: histograms, Widrow's LMS, multiplicative updates |
Thu | Sep 10 | perceptrons: theoretical limitations, randomization |
Tue | Sep 15 | multilayer perceptrons and backpropagation of error (ch 4) |
Thu | Sep 17 | more backpropagation, assignment |
Tue | Sep 22 | Kolmogorov's theorem; debugging gradient optimization |
Thu | Sep 24 | autoassociative networks and dimmensionality reduction |
Tue | Sep 29 | overfitting and generalization |
Thu | Oct 1 | more generalization |
Tue | Oct 6 | VC dimension, assignment |
Thu | Oct 8 | PAC learning, VC dimension of MLPs |
Tue | Oct 13 | convergence rate of gradient descent |
Thu | Oct 15 | (Fall break) |
Tue | Oct 20 | The Bayesian approach to generalization |
Thu | Oct 22 | (continued) assignment |
Tue | Oct 27 | Gaussian mixture models, EM, LVQ |
Thu | Oct 29 | Hierarchical Mixtures of Experts |
Tue | Nov 3 | Boltzmann machines |
Thu | Nov 5 | Fixedpoint backpropagation, MFT Boltzmann take-home quiz |
Tue | Nov 10 | (Doug Eck) Reinforcement learning |
Thu | Nov 12 | (Doug Eck) (continued) |
Tue | Nov 17 | (Doug Eck) Q-learning |
Thu | Nov 19 | Helmholtz machines |
Tue | Nov 24 | Blind source separation |
Thu | Nov 26 | (Thanksgiving) |
Tue | Dec 1 | (Michael Zibulevsky) Support Vector Machines |
Thu | Dec 3 | (Doug Eck) Recurrent Nets |
Tue | Dec 8 | Backpropagation through time, RTRL |
Thu | Dec 10 | final project presentations |
Thu | Dec 17 | 5pm, final exam due |