EEL 6814
Neural Networks for Signal Processing
Spring 2016
Instructor: Dr. Jose Principe, principe@cnel.ufl.edu
Dr. Principe's Office Hours: TBD (NEB 451)
Syllabus
Lecture notes
- Chapter 1. Data Fitting with Linear Models
- Chapter 2. Pattern Recognition
- Chapter 3. Multilayer Perceptrons
- Search
- Wiener solution
- Performance Surface
- Perceptron
- Support vector machines
- Chapter 4. Designing and Training MLPs
- Chapter 5. Function approximation
- Statistical Learning Theory and C-Loss Function
- Structural risk minimization
- Chapter 6. Hebbian learning and PCA
- Information theoretic learning (ppt)
- Introduction to Information Theory and ICA
- Chapter 11. Recurrent networks
- Renyi's Entropy
- (paper) Blind source separation using Renyi's mutual information
- The MRMI Algorithm
- NEW Deep Learning Book
- Convolutional Neural Networks
- Deep Learning Overview
- Deep Unsupervised Learning
Homeworks
- Homework 1 Due 1/21
- Homework 2 Due 2/2
- Homework 3 Due 2/16
- Homework 4 Due 3/29
- Homework 5 Due 4/21
