Classification involves taking an instance

/* (2) bag-of-words classifier: http://people.csail.mit.edu/fergus/iccv2005/bagwords.html They were short courses on ICCV 2005. */

/* Andrew Ng */

Classification on digits using neural networks

1. Check UFLDL provided by Andrew Ng
2. In this module, you are requires to do the followings:
3. First, implement the 'sparse autoencoder' section.
4. Second, get 'Vectorized implementation', 'Preprocessing: PCA and Whitening' and 'Softmax Regression' sections done.
5. Third, implement 'Self-Taught Learning and Unsupervised Feature Learning' section for digit classification.

Classification on digits using DEEP neural networks

1. Once you finish module 2, you should have a nice classification system on digit with 98% accuracy.
2. Now, the advanced task would require you to extend it to be deep neural networks
3. In this module, based on your module 1, please continue to work on 'Building Deep Networks for Classification'

/* Other related material, maybe more classic. */

ECE4580 Learning Modules