Neural Networks
- Introduction to Neural Networks and their History
Biological Neurons and Neural Networks. Artificial Neurons.
- Networks of Artificial Neurons. Single Layer Perceptrons.
- Learning and Generalization in Single Layer Perceptrons.
- Pattern Recognition and Function Approximation
- Hebbian Learning. Gradient Descent Learning.
- The Generalized Delta Rule. Practical Considerations.
- Learning in Multi-Layer Perceptrons. Back-Propagation.
- Learning with Momentum. Conjugate Gradient Learning.Bias and Variance. Under-Fitting and Over-Fitting.Improving Generalization.
- Applications of Multi-Layer Perceptrons.
- Radial Basis Function Networks: Introduction.
Radial Basis Function Networks: Algorithms.
- Radial Basis Function Networks: Applications.
Committee Machines.
- Self Organizing Maps: Fundamentals, Algorithms and Applications..
- Convolutional Neural Networks
- Convolutional Neural Networks: Applications
- Overview of More Advanced Topics