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

http://ailab/chonbuk.ac.kr

• Program codes for NN– Able to get from WWW or others

Schedule

• Overview of the class: 1 week• Introduction and Mathematical Back-

ground: 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-organiz-ing networks

Grading

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