NOC:Neural Networks for Signal Processing – I (USB)

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SKU
108108148



Media Storage Type : 32 GB USB Stick

NPTEL Subject Matter Expert : Prof. Shayan Srinivasa Garani

NPTEL Co-ordinating Institute : IISc Bangalore

NPTEL Lecture Count : 66

NPTEL Course Size : 13 GB

NPTEL PDF Text Transcription : Available and Included

NPTEL Subtitle Transcription : Available and Included (SRT)


Lecture Titles:

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

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