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Introduction to Artificial Intelligence and Machine Learning in Manufacturing

This course is designed to provide a general introduction for anyone interested in understanding and using machine learning (ML). It covers the core machine learning concepts plus more state-of-the-art approaches, planning and evaluating a machine learning project, plus outlining the potential pitfalls involved. (The aim of the course is to give an overview of what ML is and what it can do, not how to build algorithms).

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Description

Learner Profile:

This is an introductory course for learners with little or no previous data analytics knowledge, and managers who want to learn the language and tools of Machine Learning in the context of AI.

Learner prerequisites:

Candidates should, at minimum, have completed the Junior Cert with pass grades in at least five ordinary level subjects (including Maths and English).

Learning outcomes:

At the end of the course learners will be able to:

· Explain Machine Learning & Data Science.

· Understand the important Machine Learning methods and tools in the Data Science environment.

· Justify what sort of models would be appropriate for the machine learning opportunity identified.

· How to evaluate a machine learning project, plus outlining the pitfalls involved.

Course Content:

The course covers:


· What is machine learning (ML).


· When to use it.


· Types of ML.


· Artificial Intelligence (AI) use cases in manufacturing.


· Deep learning overview.


Day 1. Pre -Work


· AI Basics, 1. Introduction to AI, History of AI, Private vs. Industrial data, AI vs ML, Learning vs. Programming. Types of AI in production.


· AI Basics, 2. Neural networks, how to train neural nets, deep learning real-world applications.


· AI Basics, 3. Principle of machine learning, supervised / unsupervised learning, reinforcement learning, applications of machine learning in production.


Module 01: Understand what ML is and when to use it.


· Introduction to ML.


· ML vs AI.


· ML use cases.


Module 02: What is the workflow of a ML project


· A typical ML workflow.


· How to ask the right questions.


· Data-centric AI.


Module 03 & 04: Get familiar with a set of specific ML algorithms.


• Regression.


• Classification.


• Common problems.


• Error analysis.


• Decision trees.


• Random forests.


Module 05: Understand how Deep Learning works 


• Neural networks.


• Deep Learning .


• Module 06: Q & A.


• Where to go from here.


• Questions and feedback form.

Duration:

This course is 1 day long.


Lasts for 8.5 hours (approximately 2 hours pre-work and 6.5 hours live on-line).


Exam/Continuous Assessment details:

N/A

Certification / Awarding Body/Credits:

Certificate: CPD Certificate of Attendance (issued by Training Provider)


Awarding Body: Engineers Ireland


Credits: This course awards 8.5 hours Continuous Professional Development (CPD) - as recognised by Engineers Ireland. This is documented on the certificate.


(QQI Credits: N/A)

Progression Pathway:

N/A