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