NOC:Machine Learning for Engineering and Science Applications

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