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
- Applications of Multi-Layer Perceptrons
- Radial Basis Function Networks: Introduction and Algorithms
- Radial Basis Function Networks: Applications
- Self Organizing Maps: Fundamentals, , Algorithms and Applications.
- Convolutional Neural Networks
- Convolutional Neural Networks: Applications
- Overview of More Advanced Topics