Modules / Lectures
Module NameDownload
noc19_cs82_assignment_Week_1noc19_cs82_assignment_Week_1
noc19_cs82_assignment_Week_10noc19_cs82_assignment_Week_10
noc19_cs82_assignment_Week_11noc19_cs82_assignment_Week_11
noc19_cs82_assignment_Week_12noc19_cs82_assignment_Week_12
noc19_cs82_assignment_Week_2noc19_cs82_assignment_Week_2
noc19_cs82_assignment_Week_3noc19_cs82_assignment_Week_3
noc19_cs82_assignment_Week_4noc19_cs82_assignment_Week_4
noc19_cs82_assignment_Week_5noc19_cs82_assignment_Week_5
noc19_cs82_assignment_Week_6noc19_cs82_assignment_Week_6
noc19_cs82_assignment_Week_7noc19_cs82_assignment_Week_7
noc19_cs82_assignment_Week_8noc19_cs82_assignment_Week_8
noc19_cs82_assignment_Week_9noc19_cs82_assignment_Week_9


Sl.No Chapter Name MP4 Download
1Introduction to the Course History of Artificial IntelligenceDownload
2Overview of Machine LearningDownload
3Why Linear Algebra ? Scalars, Vectors, TensorsDownload
4Basic OperationsDownload
5NormsDownload
6Linear Combinations Span Linear IndependenceDownload
7Matrix Operations Special Matrices Matrix DecompositionsDownload
8Introduction to Probability Theory Discrete and Continuous Random VariablesDownload
9Conditional, Joint, Marginal Probabilities Sum Rule and Product Rule Bayes' TheoremDownload
10Bayes' Theorem - Simple ExamplesDownload
11Independence Conditional Independence Chain Rule Of ProbabilityDownload
12ExpectationDownload
13Variance CovarianceDownload
14Some Relations for Expectation and Covariance (Slightly Advanced)Download
15Machine Representation of Numbers, Overflow, Underflow, Condition NumberDownload
16Derivatives,Gradient,Hessian,Jacobian,Taylor SeriesDownload
17Matrix Calculus (Slightly Advanced)Download
18Optimization – 1 Unconstrained OptimizationDownload
19Introduction to Constrained OptimizationDownload
20Introduction to Numerical Optimization Gradient Descent - 1Download
21Gradient Descent – 2 Proof of Steepest Descent Numerical Gradient Calculation Stopping CriteriaDownload
22Introduction to PackagesDownload
23The Learning ParadigmDownload
24A Linear Regression ExampleDownload
25Linear Regression Least Squares Gradient DescentDownload
26Coding Linear RegressionDownload
27Generalized Function for Linear RegressionDownload
28Goodness of FitDownload
29Bias-Variance Trade OffDownload
30Gradient Descent AlgorithmsDownload
31Introduction to Week 5 (Deep Learning)Download
32Logistic RegressionDownload
33Binary Entropy cost functionDownload
34OR Gate Via ClassificationDownload
35NOR, AND, NAND GatesDownload
36XOR GateDownload
37Differentiating the sigmoidDownload
38Gradient of logistic regressionDownload
39Code for Logistic Regression Download
40Multinomial Classification- Introduction Download
41Multinomial Classification - One Hot VectorDownload
42Multinomial Classification - Softmax Download
43Schematic of multinomial logistic regressionDownload
44Biological neuronDownload
45Structure of an Artificial NeuronDownload
46Feedforward Neural NetworkDownload
47Introduction to back propDownload
48Summary of Week 05Download
49Introduction to Convolution Neural Networks (CNN)Download
50Types of convolutionDownload
51CNN Architecture Part 1 (LeNet and Alex Net)Download
52CNN Architecture Part 2 (VGG Net)Download
53CNN Architecture Part 3 (GoogleNet)Download
54CNN Architecture Part 4 (ResNet)Download
55CNN Architecture Part 5 (DenseNet)Download
56Train Network for Image ClassificationDownload
57Semantic SegmentationDownload
58Hyperparameter optimizationDownload
59Transfer LearningDownload
60Segmentation of Brain Tumors from MRI using Deep LearningDownload
61Activation FunctionsDownload
62Learning Rate decay, Weight initializationDownload
63Data Normalization Download
64Batch NormDownload
65Introduction to RNNsDownload
66Example - Sequence ClassificationDownload
67Training RNNs - Loss and BPTTDownload
68Vanishing Gradients and TBPTTDownload
69RNN ArchitecturesDownload
70LSTMDownload
71Why LSTM WorksDownload
72Deep RNNs and Bi- RNNsDownload
73Summary of RNNsDownload
74Introduction.Download
75KnnDownload
76Binary decision treesDownload
77Binary regression treesDownload
78BaggingDownload
79Random ForestDownload
80BoostingDownload
81Gradient boostingDownload
82Unsupervised learning & KmeansDownload
83Agglomerative clusteringDownload
84Probability Distributions- Gaussian, BernoulliDownload
85Covariance Matrix of Gaussian DistributionDownload
86Central Limit TheoremDownload
87Naïve BayesDownload
88MLE IntroDownload
89PCA-part 1Download
90PCA-part 2Download
91Support Vector MachinesDownload
92MLE, MAP and Bayesian RegressionDownload
93Introduction to Generative modelDownload
94Generative Adversarial Networks (GAN)Download
95Variational Auto-encoders (VAE)Download
96Applications: Cardiac MRI - Segmentation & DiagnosisDownload
97Applications: Cardiac MRI Analysis - Tensorflow code walkthroughDownload
98Introduction to Week 12Download
99Application 1 description - Fin Heat TransferDownload
100Application 1 solutionDownload
101Application 2 description - Computational Fluid DynamicsDownload
102Application 2 solutionDownload
103Application 3 description - Topology OptimizationDownload
104Application 3 solutionDownload
105Application 4 - Solution of PDE/ODE using Neural NetworksDownload
106Summary and road aheadDownload

Sl.No Chapter Name English
1Introduction to the Course History of Artificial IntelligenceDownload
Verified
2Overview of Machine LearningDownload
Verified
3Why Linear Algebra ? Scalars, Vectors, TensorsDownload
Verified
4Basic OperationsDownload
Verified
5NormsDownload
Verified
6Linear Combinations Span Linear IndependenceDownload
Verified
7Matrix Operations Special Matrices Matrix DecompositionsDownload
Verified
8Introduction to Probability Theory Discrete and Continuous Random VariablesDownload
Verified
9Conditional, Joint, Marginal Probabilities Sum Rule and Product Rule Bayes' TheoremDownload
Verified
10Bayes' Theorem - Simple ExamplesDownload
Verified
11Independence Conditional Independence Chain Rule Of ProbabilityDownload
Verified
12ExpectationDownload
Verified
13Variance CovarianceDownload
Verified
14Some Relations for Expectation and Covariance (Slightly Advanced)Download
Verified
15Machine Representation of Numbers, Overflow, Underflow, Condition NumberDownload
Verified
16Derivatives,Gradient,Hessian,Jacobian,Taylor SeriesDownload
Verified
17Matrix Calculus (Slightly Advanced)Download
Verified
18Optimization – 1 Unconstrained OptimizationDownload
Verified
19Introduction to Constrained OptimizationDownload
Verified
20Introduction to Numerical Optimization Gradient Descent - 1Download
Verified
21Gradient Descent – 2 Proof of Steepest Descent Numerical Gradient Calculation Stopping CriteriaDownload
Verified
22Introduction to PackagesDownload
Verified
23The Learning ParadigmDownload
Verified
24A Linear Regression ExampleDownload
Verified
25Linear Regression Least Squares Gradient DescentDownload
Verified
26Coding Linear RegressionDownload
Verified
27Generalized Function for Linear RegressionDownload
Verified
28Goodness of FitDownload
Verified
29Bias-Variance Trade OffDownload
Verified
30Gradient Descent AlgorithmsDownload
Verified
31Introduction to Week 5 (Deep Learning)Download
Verified
32Logistic RegressionDownload
Verified
33Binary Entropy cost functionDownload
Verified
34OR Gate Via ClassificationDownload
Verified
35NOR, AND, NAND GatesDownload
Verified
36XOR GateDownload
Verified
37Differentiating the sigmoidDownload
Verified
38Gradient of logistic regressionDownload
Verified
39Code for Logistic Regression Download
Verified
40Multinomial Classification- Introduction Download
Verified
41Multinomial Classification - One Hot VectorDownload
Verified
42Multinomial Classification - Softmax Download
Verified
43Schematic of multinomial logistic regressionDownload
Verified
44Biological neuronDownload
Verified
45Structure of an Artificial NeuronDownload
Verified
46Feedforward Neural NetworkDownload
Verified
47Introduction to back propDownload
Verified
48Summary of Week 05Download
Verified
49Introduction to Convolution Neural Networks (CNN)Download
Verified
50Types of convolutionDownload
Verified
51CNN Architecture Part 1 (LeNet and Alex Net)Download
Verified
52CNN Architecture Part 2 (VGG Net)Download
Verified
53CNN Architecture Part 3 (GoogleNet)Download
Verified
54CNN Architecture Part 4 (ResNet)Download
Verified
55CNN Architecture Part 5 (DenseNet)Download
Verified
56Train Network for Image ClassificationDownload
Verified
57Semantic SegmentationDownload
Verified
58Hyperparameter optimizationDownload
Verified
59Transfer LearningDownload
Verified
60Segmentation of Brain Tumors from MRI using Deep LearningDownload
Verified
61Activation FunctionsDownload
Verified
62Learning Rate decay, Weight initializationDownload
Verified
63Data Normalization Download
Verified
64Batch NormDownload
Verified
65Introduction to RNNsDownload
Verified
66Example - Sequence ClassificationDownload
Verified
67Training RNNs - Loss and BPTTDownload
Verified
68Vanishing Gradients and TBPTTDownload
Verified
69RNN ArchitecturesDownload
Verified
70LSTMDownload
Verified
71Why LSTM WorksDownload
Verified
72Deep RNNs and Bi- RNNsDownload
Verified
73Summary of RNNsDownload
Verified
74Introduction.Download
Verified
75KnnDownload
Verified
76Binary decision treesDownload
Verified
77Binary regression treesDownload
Verified
78BaggingDownload
Verified
79Random ForestDownload
Verified
80BoostingDownload
Verified
81Gradient boostingDownload
Verified
82Unsupervised learning & KmeansDownload
Verified
83Agglomerative clusteringDownload
Verified
84Probability Distributions- Gaussian, BernoulliDownload
Verified
85Covariance Matrix of Gaussian DistributionDownload
Verified
86Central Limit TheoremDownload
Verified
87Naïve BayesDownload
Verified
88MLE IntroDownload
Verified
89PCA-part 1Download
Verified
90PCA-part 2Download
Verified
91Support Vector MachinesDownload
Verified
92MLE, MAP and Bayesian RegressionDownload
Verified
93Introduction to Generative modelDownload
Verified
94Generative Adversarial Networks (GAN)Download
Verified
95Variational Auto-encoders (VAE)Download
Verified
96Applications: Cardiac MRI - Segmentation & DiagnosisDownload
Verified
97Applications: Cardiac MRI Analysis - Tensorflow code walkthroughDownload
Verified
98Introduction to Week 12Download
Verified
99Application 1 description - Fin Heat TransferDownload
Verified
100Application 1 solutionDownload
Verified
101Application 2 description - Computational Fluid DynamicsDownload
Verified
102Application 2 solutionDownload
Verified
103Application 3 description - Topology OptimizationDownload
Verified
104Application 3 solutionDownload
Verified
105Application 4 - Solution of PDE/ODE using Neural NetworksDownload
Verified
106Summary and road aheadDownload
Verified


Sl.No Language Book link
1EnglishDownload
2BengaliNot Available
3GujaratiNot Available
4HindiNot Available
5KannadaNot Available
6MalayalamNot Available
7MarathiNot Available
8TamilNot Available
9TeluguNot Available