Neural Networks

This is the main website for the Fall 2013 course Neural Networks.

Lecture notes and assignments are posted to: materials.

My consultation hours are: We 12-14 in room 203 or by email appointment.

Other course information is enclosed under materials.

Auxiliary Learning Materials:

• Paid Books:

1. Murphy, Machine Learning: a Probabilistic Perspective http://www.cs.ubc.ca/~murphyk/MLbook/

2. Bishop, Pattern Recognition and Machine Learning http://research.microsoft.com/en-us/um/people/cmbishop/prml/

• Free Books:

1. MacKay, Information Theory, Inference, and Learning Algorithms http://www.inference.phy.cam.ac.uk/itila/book.html

2. Hastie, Tibshirani, Friedman, The Elements of Statistical Learning http://statweb.stanford.edu/~tibs/ElemStatLearn/

• Other Materials:

1. A. Ng lecture notes: http://cs229.stanford.edu/

2. A. Ng Machine Learning on Coursera: https://www.coursera.org/course/ml

3. G. Hinton Neural Networks for Machine Learning https://www.coursera.org/course/neuralnets

Useful links:

• • http://cs229.stanford.edu/ First lectures are covered by the 1st lecture note.

  • Matrix identities, derivatives etc.: http://www.ee.ic.ac.uk/hp/staff/dmb/matrix/intro.html

  • Good numeric python distribution for Windows (Linuxes usually have all that is needed): http://code.google.com/p/winpython/

  • The paper that (re)introduced backpropagation: http://www.cs.toronto.edu/~hinton/absps/naturebp.pdf

  • The best reference on backpropagation training of networks: http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf