DAY 2:

Fundamentals of Adaptive Systems and Neural Networks

• Adaptive Systems and Linear Regression
• Analyzing linear adaptive systems
• Understanding gradient descent training

Supervised Learning

• Overview of MLPs (nonlinear extenstions to linear adaptive systems)
• Tips and tricks of the trade: MLP parameters and how to set them
• Applications of MLPs
• Genetic optimization of parameters
• Project 1: Using MLPs for classification

Unsupervised Learning

• Intro to unsupervised learning
• Hebbian learning and principal component analysis
• Competitive learning and clustering (including SOMs)

DAY 3:

Radial basis functions (RBFs)

• Introduction to unsupervised learning
• What are RBFs and why/when should you use them?
• How to use RBFs and how to set their parameters
• Hybrid unsupervised/supervised networks
• Project 2: Using a hybrid RBF/MLP for classification

Temporal processing and dynamical systems

• Adaptive signal processing fundamentals
• Temporal neural networks

DAY 4:

• Optimizing inputs, learning rates, network size, etc. 

Financial Forecasting using Neural Networks

• Introduction to prediction and the stock market 
• Optimal trading signals
• Neural network prediction
• Building a trading system
• Analyzing and optimizing the trading system 

Overview of using the advanced features and capabilities of NeuroSolutions

• Using Macros to automate tasks 
  • Introduction to macros and the MacroWizard utility
  • Recording a sequence of events
  • Using the MacroWizard editor and debugger 
  • Assigning macros to dialog components and toolbar buttons
• Customizing components using DLLs
  • Creating a new processing element activation function
  • Updating the backpropagation plane
  • Creating a new error criteria
  • Creating a new gradient search component
  • Specialized I/O
    • Creating a new file translator
    • Reading/Writing data to/from an external source