Objective: Bring data into MATLAB and organize it for analysis, including normalizing data and removing observations with missing values.
Finding Natural Patterns in Data (Clustering)
Objective: Use unsupervised learning techniques to group observations based on a set of explanatory variables and discover natural patterns in a data set.
Cluster evaluation and interpretation
Building Classification Models
Objective: Use supervised learning techniques to perform predictive modeling for classification problems. Evaluate the accuracy of a predictive model.
Training and validation
Building Regression Models
Objective: Use supervised learning techniques to perform predictive modeling for continuous response variables.
Parametric regression methods
Nonparametric regression methods
Evaluation of regression models
Creating Neural Networks
Objective: Create and train neural networks for clustering and predictive modeling. Adjust network architecture to improve performance.
Clustering with Self-Organizing Maps
Classification with feed-forward networks
Regression with feed-forward networks
Transfer Learning for Image Classification
Objective: Perform image classification using pretrained networks. Use transfer learning to train customized classification networks.
Building Convolutional Networks
Objective: Build convolutional networks from scratch. Understand how information is passed between network layers and how different types of layers work. Train networks to locate and label specific objects within images.