| Module Name | Download |
|---|---|
| Week_01_Assignment_01 | Week_01_Assignment_01 |
| Week_02_Assignment_02 | Week_02_Assignment_02 |
| Week_03_Assignment_03 | Week_03_Assignment_03 |
| Week_04_Assignment_04 | Week_04_Assignment_04 |
| Week_05_Assignment_05 | Week_05_Assignment_05 |
| Week_06_Assignment_06 | Week_06_Assignment_06 |
| Week_07_Assignment_07 | Week_07_Assignment_07 |
| Week_08_Assignment_08 | Week_08_Assignment_08 |
| Week_09_Assignment_09 | Week_09_Assignment_09 |
| Week_10_Assignment_10 | Week_10_Assignment_10 |
| Week_11_Assignment_11 | Week_11_Assignment_11 |
| Week_12_Assignment_12 | Week_12_Assignment_12 |
| Sl.No | Chapter Name | MP4 Download |
|---|---|---|
| 1 | The human brain | Download |
| 2 | Introduction to Neural Networks | Download |
| 3 | Models of a neuron | Download |
| 4 | Feedback and network architectures | Download |
| 5 | Knowledge representation | Download |
| 6 | Prior information and invariances | Download |
| 7 | Learning processes | Download |
| 8 | Perceptron 1 | Download |
| 9 | Perceptron 2 | Download |
| 10 | Batch perceptron algorithm | Download |
| 11 | Perceptron and Bayes classifier | Download |
| 12 | Linear regression 1 | Download |
| 13 | Linear regression 2 | Download |
| 14 | Linear regression 3 | Download |
| 15 | Logistic regression | Download |
| 16 | Multi-layer perceptron 1 | Download |
| 17 | Multi-layer perceptron 2 | Download |
| 18 | Back propagation 1 | Download |
| 19 | Back propagation 2 | Download |
| 20 | XOR problem | Download |
| 21 | Universal approximation function | Download |
| 22 | Complexity Regularization and Cross validation | Download |
| 23 | Convolutional Neural Networks (CNN) | Download |
| 24 | Cover’s Theorem | Download |
| 25 | Multivariate interpolation problem | Download |
| 26 | Radial basis functions (RBF) | Download |
| 27 | Recursive least squares algorithm | Download |
| 28 | Comparison of RBF with MLP | Download |
| 29 | Kernel regression using RBFs | Download |
| 30 | Kernel Functions | Download |
| 31 | Basics of constrained optimization | Download |
| 32 | Optimization with equality constraint | Download |
| 33 | Optimization with inequality constraint | Download |
| 34 | Support Vector Machines (SVM) | Download |
| 35 | Optimal hyperplane for linearly separable patterns | Download |
| 36 | Quadratic optimization for finding optimal hyperplane | Download |
| 37 | Optimal hyperplane for non-linearly separable patterns | Download |
| 38 | Inner product kernel and Mercer’s theorem | Download |
| 39 | Optimal design of an SVM | Download |
| 40 | ε-insensitive loss function | Download |
| 41 | XOR problem revisited using SVMs | Download |
| 42 | Hilbert Space | Download |
| 43 | Reproducing Kernel Hilbert Space | Download |
| 44 | Representer Theorem | Download |
| 45 | Generalized applicability of the representer theorem | Download |
| 46 | Regularization Theory | Download |
| 47 | Euler-Lagrange Equation | Download |
| 48 | Regularization Networks | Download |
| 49 | Generalized RBF networks | Download |
| 50 | XOR problem revisited using RBF | Download |
| 51 | Structural Risk Minimization | Download |
| 52 | Bias-Variance Dilemma | Download |
| 53 | Estimation of regularization parameters | Download |
| 54 | Basics of L1 regularization | Download |
| 55 | Grafting | Download |
| 56 | Kernel PCA | Download |
| 57 | Hebbian based maximum eigen filter -1 | Download |
| 58 | Hebbian based maximum eigen filter -2 | Download |
| 59 | Hebbian based maximum eigen filter - 3 | Download |
| 60 | VC dimension | Download |
| 61 | Autoencoders | Download |
| 62 | Denoising Autoencoders | Download |
| 63 | Demo – Perceptron | Download |
| 64 | Demo – Motivation for CNN | Download |
| 65 | Back propagation in Convolutional Neural Network | Download |
| 66 | Ethics in AI research and coverage summary | Download |
| Sl.No | Chapter Name | English |
|---|---|---|
| 1 | The human brain | Download To be verified |
| 2 | Introduction to Neural Networks | Download To be verified |
| 3 | Models of a neuron | Download To be verified |
| 4 | Feedback and network architectures | Download To be verified |
| 5 | Knowledge representation | Download To be verified |
| 6 | Prior information and invariances | Download To be verified |
| 7 | Learning processes | Download To be verified |
| 8 | Perceptron 1 | Download To be verified |
| 9 | Perceptron 2 | Download To be verified |
| 10 | Batch perceptron algorithm | Download To be verified |
| 11 | Perceptron and Bayes classifier | Download To be verified |
| 12 | Linear regression 1 | Download To be verified |
| 13 | Linear regression 2 | Download To be verified |
| 14 | Linear regression 3 | Download To be verified |
| 15 | Logistic regression | Download To be verified |
| 16 | Multi-layer perceptron 1 | Download To be verified |
| 17 | Multi-layer perceptron 2 | PDF unavailable |
| 18 | Back propagation 1 | PDF unavailable |
| 19 | Back propagation 2 | PDF unavailable |
| 20 | XOR problem | PDF unavailable |
| 21 | Universal approximation function | PDF unavailable |
| 22 | Complexity Regularization and Cross validation | PDF unavailable |
| 23 | Convolutional Neural Networks (CNN) | PDF unavailable |
| 24 | Cover’s Theorem | PDF unavailable |
| 25 | Multivariate interpolation problem | PDF unavailable |
| 26 | Radial basis functions (RBF) | PDF unavailable |
| 27 | Recursive least squares algorithm | PDF unavailable |
| 28 | Comparison of RBF with MLP | PDF unavailable |
| 29 | Kernel regression using RBFs | PDF unavailable |
| 30 | Kernel Functions | PDF unavailable |
| 31 | Basics of constrained optimization | PDF unavailable |
| 32 | Optimization with equality constraint | PDF unavailable |
| 33 | Optimization with inequality constraint | PDF unavailable |
| 34 | Support Vector Machines (SVM) | PDF unavailable |
| 35 | Optimal hyperplane for linearly separable patterns | PDF unavailable |
| 36 | Quadratic optimization for finding optimal hyperplane | PDF unavailable |
| 37 | Optimal hyperplane for non-linearly separable patterns | PDF unavailable |
| 38 | Inner product kernel and Mercer’s theorem | PDF unavailable |
| 39 | Optimal design of an SVM | PDF unavailable |
| 40 | ε-insensitive loss function | PDF unavailable |
| 41 | XOR problem revisited using SVMs | PDF unavailable |
| 42 | Hilbert Space | PDF unavailable |
| 43 | Reproducing Kernel Hilbert Space | PDF unavailable |
| 44 | Representer Theorem | PDF unavailable |
| 45 | Generalized applicability of the representer theorem | PDF unavailable |
| 46 | Regularization Theory | PDF unavailable |
| 47 | Euler-Lagrange Equation | PDF unavailable |
| 48 | Regularization Networks | PDF unavailable |
| 49 | Generalized RBF networks | PDF unavailable |
| 50 | XOR problem revisited using RBF | PDF unavailable |
| 51 | Structural Risk Minimization | PDF unavailable |
| 52 | Bias-Variance Dilemma | PDF unavailable |
| 53 | Estimation of regularization parameters | PDF unavailable |
| 54 | Basics of L1 regularization | PDF unavailable |
| 55 | Grafting | PDF unavailable |
| 56 | Kernel PCA | PDF unavailable |
| 57 | Hebbian based maximum eigen filter -1 | PDF unavailable |
| 58 | Hebbian based maximum eigen filter -2 | PDF unavailable |
| 59 | Hebbian based maximum eigen filter - 3 | PDF unavailable |
| 60 | VC dimension | PDF unavailable |
| 61 | Autoencoders | PDF unavailable |
| 62 | Denoising Autoencoders | PDF unavailable |
| 63 | Demo – Perceptron | PDF unavailable |
| 64 | Demo – Motivation for CNN | PDF unavailable |
| 65 | Back propagation in Convolutional Neural Network | PDF unavailable |
| 66 | Ethics in AI research and coverage summary | PDF unavailable |
| Sl.No | Language | Book link |
|---|---|---|
| 1 | English | Not Available |
| 2 | Bengali | Not Available |
| 3 | Gujarati | Not Available |
| 4 | Hindi | Not Available |
| 5 | Kannada | Not Available |
| 6 | Malayalam | Not Available |
| 7 | Marathi | Not Available |
| 8 | Tamil | Not Available |
| 9 | Telugu | Not Available |