NPTEL : Neural Networks and Applications (Electronics and Communication Engineering)

Co-ordinators : Prof. Somnath Sengupta


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