| 1 | Introduction to Artificial Neural Networks | PDF unavailable |
| 2 | Artificial Neuron Model and Linear Regression | PDF unavailable |
| 3 | Gradient Descent Algorithm | PDF unavailable |
| 4 | Nonlinear Activation Units and Learning Mechanisms | PDF unavailable |
| 5 | Learning Mechanisms-Hebbian,Competitive,Boltzmann | PDF unavailable |
| 6 | Associative memory | PDF unavailable |
| 7 | Associative Memory Model | PDF unavailable |
| 8 | Condition for Perfect Recall in Associative Memory | PDF unavailable |
| 9 | Statistical Aspects of Learning | PDF unavailable |
| 10 | V.C. Dimensions: Typical Examples | PDF unavailable |
| 11 | Importance of V.C. Dimensions Structural Risk Minimization | PDF unavailable |
| 12 | Single-Layer Perceptions | PDF unavailable |
| 13 | Unconstrained Optimization: Gauss-Newton's Method | PDF unavailable |
| 14 | Linear Least Squares Filters | PDF unavailable |
| 15 | Least Mean Squares Algorithm | PDF unavailable |
| 16 | Perceptron Convergence Theorem | PDF unavailable |
| 17 | Bayes Classifier & Perceptron: An Analogy | PDF unavailable |
| 18 | Bayes Classifier for Gaussian Distribution | PDF unavailable |
| 19 | Back Propagation Algorithm | PDF unavailable |
| 20 | Practical Consideration in Back Propagation Algorithm | PDF unavailable |
| 21 | Solution of Non-Linearly Separable Problems Using MLP | PDF unavailable |
| 22 | Heuristics For Back-Propagation | PDF unavailable |
| 23 | Multi-Class Classification Using Multi-layered Perceptrons | PDF unavailable |
| 24 | Radial Basis Function Networks: Cover's Theorem | PDF unavailable |
| 25 | Radial Basis Function Networks: Separability & Interpolation | PDF unavailable |
| 26 | Posed Surface Reconstruction | PDF unavailable |
| 27 | Solution of Regularization Equation: Greens Function | PDF unavailable |
| 28 | Use of Greens Function in Regularization Networks | PDF unavailable |
| 29 | Regularization Networks and Generalized RBF | PDF unavailable |
| 30 | Comparison Between MLP and RBF | PDF unavailable |
| 31 | Learning Mechanisms in RBF | PDF unavailable |
| 32 | Introduction to Principal Components and Analysis | PDF unavailable |
| 33 | Dimensionality reduction Using PCA | PDF unavailable |
| 34 | Hebbian-Based Principal Component Analysis | PDF unavailable |
| 35 | Introduction to Self Organizing Maps | PDF unavailable |
| 36 | Cooperative and Adaptive Processes in SOM | PDF unavailable |
| 37 | Vector-Quantization Using SOM | PDF unavailable |