Machine Learning Made Easy

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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 unsupervised learning (clustering).

Highlights include

Accessing, exploring, analyzing, and visualizing data in MATLAB
Using the Classification Learner app and functions in the Statistics and Machine Learning Toolbox to perform common machine learning tasks such as:
Feature selection and feature transformation
Specifying cross-validation schemes
Training a range of classification models, including support vector machines (SVMs), boosted and bagged decision trees, k-nearest neighbor, and discriminant analysis
Performing model assessment and model comparisons using confusion matrices and ROC curves to help choose the best model for your data
Integrating trained models into applications such as computer vision, signal processing, and data analytics.