NOC:Neural Networks for Signal Processing-I

Lecture 1 - The human brain

Lecture 2 - Introduction to Neural Networks

Lecture 3 - Models of a neuron

Lecture 4 - Feedback and network architectures

Lecture 5 - Knowledge representation

Lecture 6 - Prior information and invariances

Lecture 7 - Learning processes

Lecture 8 - Perceptron - 1

Lecture 9 - Perceptron - 2

Lecture 10 - Batch perceptron algorithm

Lecture 11 - Perceptron and Bayes classifier

Lecture 12 - Linear regression - 1

Lecture 13 - Linear regression - 2

Lecture 14 - Linear regression - 3

Lecture 15 - Logistic regression

Lecture 16 - Multi-layer perceptron - 1

Lecture 17 - Multi-layer perceptron - 2

Lecture 18 - Back propagation - 1

Lecture 19 - Back propagation - 2

Lecture 20 - XOR problem

Lecture 21 - Universal approximation function

Lecture 22 - Complexity Regularization and Cross validation

Lecture 23 - Convolutional Neural Networks (CNN)

Lecture 24 - Cover’s Theorem

Lecture 25 - Multivariate interpolation problem

Lecture 26 - Radial basis functions (RBF)

Lecture 27 - Recursive least squares algorithm

Lecture 28 - Comparison of RBF with MLP

Lecture 29 - Kernel regression using RBFs

Lecture 30 - Kernel Functions

Lecture 31 - Basics of constrained optimization

Lecture 32 - Optimization with equality constraint

Lecture 33 - Optimization with inequality constraint

Lecture 34 - Support Vector Machines (SVM)

Lecture 35 - Optimal hyperplane for linearly separable patterns

Lecture 36 - Quadratic optimization for finding optimal hyperplane

Lecture 37 - Optimal hyperplane for non-linearly separable patterns

Lecture 38 - Inner product kernel and Mercer’s theorem

Lecture 39 - Optimal design of an SVM

Lecture 40 - ε-insensitive loss function

Lecture 41 - XOR problem revisited using SVMs

Lecture 42 - Hilbert Space

Lecture 43 - Reproducing Kernel Hilbert Space

Lecture 44 - Representer Theorem

Lecture 45 - Generalized applicability of the representer theorem

Lecture 46 - Regularization Theory

Lecture 47 - Euler-Lagrange Equation

Lecture 48 - Regularization Networks

Lecture 49 - Generalized RBF networks

Lecture 50 - XOR problem revisited using RBF

Lecture 51 - Structural Risk Minimization

Lecture 52 - Bias-Variance Dilemma

Lecture 53 - Estimation of regularization parameters

Lecture 54 - Basics of L1 regularization

Lecture 55 - Grafting

Lecture 56 - Kernel PCA

Lecture 57 - Hebbian based maximum eigen filter - 1

Lecture 58 - Hebbian based maximum eigen filter - 2

Lecture 59 - Hebbian based maximum eigen filter - 3

Lecture 60 - VC dimension

Lecture 61 - Autoencoders

Lecture 62 - Denoising Autoencoders

Lecture 63 - Demo - Perceptron

Lecture 64 - Demo - Motivation for CNN

Lecture 65 - Back propagation in Convolutional Neural Network

Lecture 66 - Ethics in AI research and coverage summary