Lecture Plan of Neural Networks 2015

Purpose From introduction to advanced topic To cover various NN models –Basic models Multilayer Perceptron Hopfield model and associative memory Self-organizing models Stochastic models –Deep learning models Deep belief network Denoising Autoencoder Convolutional NN Sparse connection Learn how to apply NN in real problems

Lecture Material Slides or notes –To be acquired from WWW –Will make or get from WWW and post at Program codes for NN –Able to get from WWW or others

Schedule Overview of the class: 1 week Introduction and Mathematical Background: 1 week Various models of NNs: 4 weeks Deep Learning Models: 4 weeks Paper reading and presentation: 3 weeks Project presentation: 2 weeks

Relation to Other Courses Machine learning: –Neural networks Pattern recognition: –PCA, support-vector machines, radial basis functions (Relatively) unique to this course: –in depth treatment of single/multilayer networks, deep learning, or self-organizing networks

Grading Presence 10% Term project 30% Presentation 30% Final Exam(Take-home) 30%