INTRODUCTION TO SUPERVISED AND UNSUPERVISED MACHINE LEARNINGWhat is Machine Learning
Supervised vs Unsupervised Learning
Linear Regression
Logistic Regression
K-Means
Principal Component Analysis

FEED-FORWARD NEURAL NETWORKS
From Logistic Regression to Single Neuron Representation
Feed-Forward Neural Networks
Back-Propagation
Batch, Stochastic and Mini-Batch Gradient Descent
Workflow and Examples

BIAS AND VARIANCE IN NEURAL NETWORKS
Overfitting and Underfitting, Bias and Variance
Regularization
Batch Normalization
The Need for Machine-Learning Diagnostics
Training Set, Validation Set and Test Set
K-Fold Validation

CONVOLUTIONAL NEURAL NETWORKS
Convolution Layers: Definition and Notations
Pooling and Fully-Connected Layers
Transfer Learning
Large Datasets and Well-Known Networks
Examples and Exercises

RECURRENT NETWORKS
Recurrent Networks
Long Short Term Memory (LSTMs)
GENERATIVE NETWORKS
Autoencoders
Variational Autoencoders
Generative Neural Networks