Machine Learning

Topics include: 

1. Organizing and preprocessing data
2. Clustering data
3. Creating classification and regression models
4. Interpreting and evaluating models
5. Simplifying data sets
6. Using ensembles to improve model performance

Objective: 

1. Bring data into MATLAB and organize it for analysis, including normalizing data and removing observations with missing values.  Data types, Table, Categorical data, and Data preparation.
2. Use unsupervised learning techniques to group observations based on a set of explanatory variables and discover natural patterns in a data set. Unsupervised learning, Clustering methods and Cluster evaluation and interpretation.
3. Use supervised learning techniques to perform predictive modeling for classification problems. Evaluate the accuracy of a predictive model. Supervised learning, Training and validation and Classification methods
4. Reduce the dimensionality of a data set. Improve and simplify machine learning models. Cross validation, Feature transformation, Feature selection and Ensemble learning.
5. Use supervised learning techniques to perform predictive modeling for continuous response variables. Parametric regression methods, Nonparametric regression methods and Evaluation of regression models
6. 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 and Regression with feed-forward networks

For more details about the course contents: click here