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Training

Statistics and Machine Learning Path

Training:

Statistical and machine learning techniques are used across a range of industries to describe, analyse and model data. This learning path is designed to teach attendees how to apply statistical methods and machine learning techniques in the MATLAB environment, while also building essential skills in data handling and manipulation.

Learning Path Duration: 6 – 9 training days

 

Prerequisite

MATLAB Fundamentals (CPD Accredited)

At the core of statistical analysis is the need to work efficiently with data. This course provides a comprehensive introduction to the MATLAB language, which has been designed for easy manipulation of datasets. The course will ensure that you have the skills you need to begin building models of your own data. No prior programming experience or knowledge of MATLAB is assumed.

Core

Statistical Methods in MATLAB

This course provides hands-on experience with performing statistical data analysis. Examples and exercises demonstrate the use of appropriate MATLAB and Statistics and Machine Learning Toolbox functionality throughout the analysis process, from importing and organising data, to exploratory analysis, to confirmatory analysis and simulation.

Machine Learning with MATLAB

This course focuses on data analytics and machine learning techniques in MATLAB using functionality within Statistics and Machine Learning Toolbox and Neural Network Toolbox. The course demonstrates the use of unsupervised learning to discover features in large data sets and supervised learning to build predictive models. Examples and exercises highlight techniques for visualisation and evaluation of results.

Supplementary

Parallel Computing with MATLAB

Machine Learning often involves working with “big data” or time consuming training algorithms. This course introduces tools and techniques for distributing code and writing parallel algorithms in MATLAB. The course shows how to use Parallel Computing Toolbox to speed up existing code and scale up across multiple computers using MATLAB Distributed Computing Server (MDCS). Attendees who are working with long-running simulations, or large data sets, will benefit from the hands-on demonstrations and exercises in the course.

Building Interactive Applications in MATLAB

Machine Learning often leads to the creation of tools or “apps” that can be used by clients or colleagues to make predictions or gain insight into historical data. This course demonstrates how to create an interactive user interface for your programmes in MATLAB. This user interface can be used from within the MATLAB environment, or compiled to work as a standalone application outside of MATLAB. No prior experience of programming graphical interfaces is required.

March 25
June 21
May 31
June 3