NOC:Machine Learning for Engineering and Science Applications

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106106198



Media Storage Type : 64 GB USB Stick (2 Nos.)

NPTEL Subject Matter Expert : Prof. Ganapathy, Prof. Balaji Srinivasan

NPTEL Co-ordinating Institute : IIT Madras

NPTEL Lecture Count : 106

NPTEL Course Size : 69 GB

NPTEL PDF Text Transcription : Available and Included

NPTEL Subtitle Transcription : Available and Included (SRT)


Lecture Titles:

Lecture 1 - Introduction to the Course History of Artificial Intelligence
Lecture 2 - Overview of Machine Learning
Lecture 3 - Why Linear Algebra ? Scalars, Vectors, Tensors
Lecture 4 - Basic Operations
Lecture 5 - Norms
Lecture 6 - Linear Combinations Span Linear Independence
Lecture 7 - Matrix Operations Special Matrices Matrix Decompositions
Lecture 8 - Introduction to Probability Theory Discrete and Continuous Random Variables
Lecture 9 - Conditional, Joint, Marginal Probabilities Sum Rule and Product Rule Bayes' Theorem
Lecture 10 - Bayes' Theorem - Simple Examples
Lecture 11 - Independence Conditional Independence Chain Rule Of Probability
Lecture 12 - Expectation
Lecture 13 - Variance Covariance
Lecture 14 - Some Relations for Expectation and Covariance (Slightly Advanced)
Lecture 15 - Machine Representation of Numbers, Overflow, Underflow, Condition Number
Lecture 16 - Derivatives,Gradient,Hessian,Jacobian,Taylor Series
Lecture 17 - Matrix Calculus (Slightly Advanced)
Lecture 18 - Optimization 1 Unconstrained Optimization
Lecture 19 - Introduction to Constrained Optimization
Lecture 20 - Introduction to Numerical Optimization Gradient Descent - 1
Lecture 21 - Gradient Descent 2 Proof of Steepest Descent Numerical Gradient Calculation Stopping Criteria
Lecture 22 - Introduction to Packages
Lecture 23 - The Learning Paradigm
Lecture 24 - A Linear Regression Example
Lecture 25 - Linear Regression Least Squares Gradient Descent
Lecture 26 - Coding Linear Regression
Lecture 27 - Generalized Function for Linear Regression
Lecture 28 - Goodness of Fit
Lecture 29 - Bias-Variance Trade Off
Lecture 30 - Gradient Descent Algorithms
Lecture 31 - Introduction to Week 5 (Deep Learning)
Lecture 32 - Logistic Regression
Lecture 33 - Binary Entropy cost function
Lecture 34 - OR Gate Via Classification
Lecture 35 - NOR, AND, NAND Gates
Lecture 36 - XOR Gate
Lecture 37 - Differentiating the sigmoid
Lecture 38 - Gradient of logistic regression
Lecture 39 - Code for Logistic Regression
Lecture 40 - Multinomial Classification - Introduction
Lecture 41 - Multinomial Classification - One Hot Vector
Lecture 42 - Multinomial Classification - Softmax
Lecture 43 - Schematic of multinomial logistic regression
Lecture 44 - Biological neuron
Lecture 45 - Structure of an Artificial Neuron
Lecture 46 - Feedforward Neural Network
Lecture 47 - Introduction to back prop
Lecture 48 - Summary of Week 05
Lecture 49 - Introduction to Convolution Neural Networks (CNN)
Lecture 50 - Types of convolution
Lecture 51 - CNN Architecture Part 1 (LeNet and Alex Net)
Lecture 52 - CNN Architecture Part 2 (VGG Net)
Lecture 53 - CNN Architecture Part 3 (GoogleNet)
Lecture 54 - CNN Architecture Part 4 (ResNet)
Lecture 55 - CNN Architecture Part 5 (DenseNet)
Lecture 56 - Train Network for Image Classification
Lecture 57 - Semantic Segmentation
Lecture 58 - Hyperparameter optimization
Lecture 59 - Transfer Learning
Lecture 60 - Segmentation of Brain Tumors from MRI using Deep Learning
Lecture 61 - Activation Functions
Lecture 62 - Learning Rate decay, Weight initialization
Lecture 63 - Data Normalization
Lecture 64 - Batch Norm
Lecture 65 - Introduction to RNNs
Lecture 66 - Example - Sequence Classification
Lecture 67 - Training RNNs - Loss and BPTT
Lecture 68 - Vanishing Gradients and TBPTT
Lecture 69 - RNN Architectures
Lecture 70 - LSTM
Lecture 71 - Why LSTM Works
Lecture 72 - Deep RNNs and Bi- RNNs
Lecture 73 - Summary of RNNs
Lecture 74 - Introduction.
Lecture 75 - Knn
Lecture 76 - Binary decision trees
Lecture 77 - Binary regression trees
Lecture 78 - Bagging
Lecture 79 - Random Forest
Lecture 80 - Boosting
Lecture 81 - Gradient boosting
Lecture 82 - Unsupervised learning and Kmeans
Lecture 83 - Agglomerative clustering
Lecture 84 - Probability Distributions- Gaussian, Bernoulli
Lecture 85 - Covariance Matrix of Gaussian Distribution
Lecture 86 - Central Limit Theorem
Lecture 87 - Naïve Bayes
Lecture 88 - MLE Intro
Lecture 89 - PCA - Part 1
Lecture 90 - PCA - Part 2
Lecture 91 - Support Vector Machines
Lecture 92 - MLE, MAP and Bayesian Regression
Lecture 93 - Introduction to Generative model
Lecture 94 - Generative Adversarial Networks (GAN)
Lecture 95 - Variational Auto-encoders (VAE)
Lecture 96 - Applications: Cardiac MRI - Segmentation and Diagnosis
Lecture 97 - Applications: Cardiac MRI Analysis - Tensorflow code walkthrough
Lecture 98 - Introduction to Week 12
Lecture 99 - Application 1 description - Fin Heat Transfer
Lecture 100 - Application 1 solution
Lecture 101 - Application 2 description - Computational Fluid Dynamics
Lecture 102 - Application 2 solution
Lecture 103 - Application 3 description - Topology Optimization
Lecture 104 - Application 3 solution
Lecture 105 - Application 4 Solution of PDE/ODE using Neural Networks
Lecture 106 - Summary and road ahead

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