Course Info
Code IND02-112
Duration 5 Days
Format Classroom
Course Summary
The development of new technologies has greatly shaped the way humanity has evolved, and this change is never-ending as the discovery of technology is constant. A newer technology that is growing increasingly popular is artificial intelligence. In many ways, artificial intelligence systems have already been implemented into our day-to-day lives, in ways that have most likely gone unnoticed.
From the perspective of an organisation however, artificial intelligence has made momentous improvements to business functions. Implementing an AI system can greatly increase cost effectiveness, reduce the need for human employees and maximise productivity.
Despite the sudden rise in use, artificial intelligence systems can be complex, so it is crucial that those involved in their development are highly competent on the principles, algorithms, clustering, and data classification. These factors are integral to an AI system, as they will dictate how effectively the system will be able to conduct its purpose.
AI has the potential to completely shift the modern world and how we carry out our lives. With constant improvements on the integral systems, the limitations of these systems are being erased. From an organisational perspective, it is important to understand the extent of which AI will be used and to find the safe balance of AI functions and human employees
During this course, you’ll learn:
To understand the vitality of artificial intelligence in modern society.
To identify potential areas where artificial intelligence can improve functions.
To assess how artificial intelligence can increase an organisation’s cost effectiveness, productivity, optimise resources and reduce risks.
To analyse the concepts, principles, and processes of artificial intelligence.
To effectively plan, design, implement and monitor an artificial intelligence system.
To create an artificial intelligence system that is highly adaptable to external and internal influences of change.
To examine various intelligent agents and how they can be integrated into the artificial intelligence system.
To apply processes of regression, classification, clustering, retrieval, recommender systems and deep learning.
This course is designed for anyone who wishes to develop their knowledge surrounding artificial intelligence. It would be most beneficial for:
Artificial Intelligence Engineers
Machine Learning Engineers
Operations Managers
Data Analysts
IT Professionals
Planning and Strategy Managers
Project Managers
Chief Information Officers (CIOs)
This course uses a variety of adult learning styles to aid full understanding and comprehension. Participants will investigate existing artificial intelligence systems to highlight key processes, algorithms, and intelligent agents.
They will be supplied with the best tools and equipment necessary to successfully partake in the given learning exercises. Combined with presentations, practical activities and case studies, the participants will have a full opportunity to develop a full and comprehensive understanding of the taught content. Participants will also be able to plan and design their own artificial intelligence system that relates to their specific roles.
Course Content & Outline
Section 1
Introduction to Artificial Intelligence
Understanding the basic principles and concepts of artificial intelligence.
Describing the process of state space search.
Graph theory and information of state space search.
Assessing how state space search is an ideal method of problem solving.
Identifying how state space search is integral to the design and function of artificial intelligence systems.
Hill climbing and minimax algorithms.
Section 2
Machine Learning
The fundamentals of machine learning.
Understanding the different types of information clustering – partitioning and hierarchical.
Examining the benefits and limitations of the types of clustering.
The various types of classification algorithms and how they are used – logistic regression, naïve byes, k-nearest neighbours, decision tree and support vector machines.
How machine learning is integral to artificial intelligence.
Section 3
Decision Making
Describing the purpose of an intelligent agent.
Common uses for intelligent agents.
Different types of intelligent agents – reflex, model based, goal based, utility based and learning agents.
Utilising deep learning neural networks (DNN) and artificial neural networks (ANN) to mimic human intelligence.
Identifying circumstances where it is best to use DNN or ANN.
Section 4
Genetic Algorithms and Fuzzy Logic
Achieving maximum optimisation using genetic algorithms.
Characteristics of a genetic algorithm - chromosomes, genes, selection, mutation, and crossover.
Balancing fuzzy sets with fuzzy rules.
Fuzzy logic vs probability.
Section 5
The Future of AI
Common uses of AI in modern organisations.
Analysing the past and present uses of Ai to accurately predict the future of AI development.
Evaluating whether AI is making human jobs in society redundant.
The potential risks of standardising AI systems.
AI's Evolution: 10 Predictions for the Future of AI
Embark on a voyage into AI's future, where innovation intertwines with concerns. From healthcare to climate, AI promises advancements, yet ethical dilemmas, bias, and job displacement demand vigilance. Join us as we navigate the potential and challenges that AI's path unfolds.
Embark on a voyage into AI's future, where innovation intertwines with concerns. From healthcare to climate, AI promises advancements, yet ethical dilemmas, bias, and job displacement demand vigilance. Join us as we navigate the potential and challenges that AI's path unfolds.
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