Machine learning is ubiquitous. From medical diagnosis, speech, and handwriting recognition to automated trading and movie recommendations, machine learning techniques are being used to make critical business and life decisions every moment of the day. Each machine learning problem is unique, so it can be challenging to manage raw data, identify key features that impact your model, train multiple models, and perform model assessments.
In this session we explore the fundamentals of machine learning using MATLAB®. Through several examples we review typical workflows for both supervised learning (classification and regression) and unsupervised learning (clustering).
Highlights include:
- Accessing, exploring, analyzing, and visualizing data
- Training a range of machine learning models, including linear regression models, support vector machines (SVMs), boosted and bagged decision trees, k-nearest neighbor, Naïve Bayes, discriminant analysis, and neural networks
- Performing model assessment and model comparisons using statistical tests to help choose the best model for your data
- Improving models using feature selection and feature transformation techniques
- Sharing results in the form of reports or integrating models within production environments
