Artificial Intelligence Overview


In this course, you will learn how to define and differentiate between Artificial Intelligence (AI) and Machine Learning (ML), the philosophies underpinning AI, and how to identify and use appropriate tools to build AI/ML modelling for real-world problem-solving. You will learn how to construct simple correlation and regression models before constructing more complex modelling to solve problems using a variety of evolutionary algorithms.


Course Overview

This short course covers:  

Weeks 1-3

  • AI Foundations: Artificial Intelligence (AI) vs. Machine Learning (ML)
  • Learning paradigms: Supervised learning, unsupervised learning and reinforcement learning.
  • Real-life applications of ML – case studies and Search algorithms

Weeks 4-6

  • Linear models for classification
  • Linear models for regression
  • Lazy learning methods: K nearest neighbour method

Weeks 7-10 

  • Nonlinear methods, Neural networks
  • Neural networks, MLP, Back Propagation
  • Deep learning
  • Ethical considerations of AI and ML


Course Aims

Gain skills:

  • Discover the fundamentals of artificial intelligence and machine learning.
  • Introductory, hands-on experience with some tools and techniques.

Gain knowledge:

  • Gain an appreciation of where AI/ML technologies fit within industry business processes.



The learner will leave the session with:

  • Organisations can benefit from understanding if AI/ML techniques apply to solving their business problems or improving their business processes.
  • Organisations can remain competitive where necessary by using AI/ML processes to augment existing processes.
  • The learner will benefit through the discovery and appreciation of AI/ML technologies
  • The learner will apply limited hands-on skills to get started with AI/ML technologies in their organisation.
  • The learner will know the wider context of AI/ML within industry and society.



No prior experience is necessary, although a background in IT, computing, or computer science would be beneficial.

Some basic mathematical or statistical knowledge at a GCSE higher level or equivalent would be useful, but not essential.



After completing the course, learners will receive a certificate of attendance. Learners who complete the assignment will also receive 20 credits, which can be used for future learning. 



The Staffordshire E-Skills & Entrepreneurship Gateway (SEGway) is part funded by the European Social Fund.

Delivery method
Distance learning

10 weeks (one 2-hour session per week) 

Start date

Thursday 9 February 2023

School of Digital, Technologies and Arts
Certificate of Attendance

Entry requirements

This training course is exclusively available to Stoke-on-Trent and Staffordshire based students, graduates and businesses with a maximum of 250 employees (SMEs).


This course is free to all Staffordshire based students, graduates, start-ups and small to medium-sized businesses (SMEs). 


Joideep Banerjee

P/T Lecturer (Hrly) - Computing

How to apply

To apply for this course, please email

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