Machine Learning Made Easy

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Shashank Prasanna, MathWorks

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.

About the Presenter: Shashank Prasanna is a product marketing manager at MathWorks, where he focuses on MATLAB and add-on products for statistics, machine learning, and data analytics. Prior to joining MathWorks, Shashank worked on software design and development at Oracle. Shashank holds an M.S. in electrical engineering from Arizona State University.

Product Focus

  • Statistics and Machine Learning Toolbox
  • Neural Network Toolbox
  • Parallel Computing Toolbox
  • Fuzzy Logic Toolbox
  • MATLAB

Recorded: 19 Mar 2015