Artificial Intelligence and Machine Learning in Business


Course Name

Artificial Intelligence and Machine Learning in Business

Course Code

PD-IT – E5

Number of Contact Hours

45 hours

Credit Hours

3 Credit Hour

Duration and Frequency

  • 15 sessions 
  • Each session = 3 hours
  • Frequency: daily Monday to Friday
  • Duration: 3 weeks

Mode of Delivery

  • Online/ On Campus/ Hybrid 


Professional Development – 

E – Information Technology


This course is designed with the objective of empowering all executives to be able to make informed decisions about the benefits of AI within all areas of their organizations, and to be able to understand the strategic implications of AI within a new digital ecosystem.

Organizations that can rapidly sense and respond to opportunities will seize the advantage in the AI-enabled landscape. Understanding, organizing, integrating and delivering AI is a key issue today. Business must be clear about the use and value of AI to avoid chasing an unachievable and expensive dream. AI carries with it many implications. Jobs change dramatically, current skill become obsolete and displaced, there may exist resistance to change and unrealistic fear of robots taking over as well as other aspects of automation.

This course will provide a broad understanding of the basic techniques for building intelligent computer systems and an understanding of how AI is applied to problems.



Learning Objectives of this course are:

  • Introduction to Artificial Intelligence and intelligent agents, history of Artificial Intelligence
  • Building intelligent agents (search, games, logic, constraint satisfaction problems)
  • Machine Learning algorithms
  • Applications of AI (Natural Language Processing, Robotics/Vision)
  • Solving real AI problems through programming with Python



On completion of this course, participants are expected to be able to:

  • Define the most common terms used in the field of AI and ML.
  • Discuss the history highlights of AI and ML in relation to how you can apply the concepts today.
  • Identify the foundational theories upon which AI and ML have been built.
  • Exhibit the different uses of machine intelligence in the form of AI and ML.
  • Identify the foundational differences between Human and Machine Learning.
  • Explain the optimal way to combine Human and Machine Learning.
  • Identify in which category a specific ML algorithm will fall.
  • Create a data acquisition strategy based on each type of algorithm.
  • Devise a Human Resources requirement for each type of algorithm.
  • Create a software and hardware strategy to support each of these ML algorithms approaches.
  • Assess the various methodologies used in micro narrative enquiry.
  • Explain the similarities and differences of the insights that can be obtained from Narrative Enquiry and ML gathered market insights.
  • Explain the impact of Digital Ecosystems on AI, and vice-versa.
  • Comprehend how we can use AI to make internal processes more efficient and obtain deeper market insights.
  • Identify the main differences between AI-created User Behavior Mapping vs. Traditional Market Research.
  • Explain why Machine Learning will provide new categorizations of markets based on behavioral patterns
  • Compare a number of AI products that will help your organization to be more productive and effective.
  • Explain the process you can follow to implement these AI solutions within your organization
  • Compare the various AI tools and libraries available in Python.
  • Identify which software tools and libraries can be used for a specific AI project.
  • Apply existing AI algorithms to AI problems.
  • Create a test bed for AI experimentation.
  • Construct a project in which you can use AI to provide competitive insights.
  • Map the skill sets required in your organization to implement and maintain a continued AI expertise.
  • Identify new business opportunities that will lay ahead in the medium and long term that will be made possible via AI and ML.


Course Outline:

An introduction to Artificial Intelligence (AI) and Machine Learning (ML) terminology: 

o The terminology used in the field of AI and ML is often far removed from the language used by managers and executives on a daily basis. The terminology is however key in understanding the field of Machine Learning and Artificial Intelligence. 


The History of AI and ML

o The terms AI and ML have become much more widespread than ever before. They are often used interchangeably and promise all sorts of applications from smarter home appliances to robots taking our jobs.

o But while AI and ML are strongly related, they are not quite the same thing. AI is a branch of computer science attempting to build machines capable of intelligent behavior, while Stanford University defines machine learning (ML) as “the science of getting computers to act without being explicitly programmed”.


The difference between Human Learning (HL) and ML:

o The most valuable resource we have in the universe is intelligence, which is simply information and computation. However, in order to be effective, technological intelligence has to be communicated in a way that helps humans take advantage of the knowledge gained.

o One of the most important departure points in the AI and ML journey, is to develop a deeper understanding of the difference between HL and ML. This understanding is one of the most important foundations of this course, as it highlights the differences of the outcomes of each approach. It also allows us to understand the limitations of each approach and allows us to optimize our endeavors in order to obtain the optimal value of each approach. In the end, it is the synergy that will be created in the joint understanding of the outcomes of HL and ML that will lead to the overall success of AI in the workplace.


AI created User Behavior Mapping vs. Narrative Enquiry

o Narrative inquiry is a way of understanding and inquiring into experience through “collaboration between researcher and participants, over time, in a place or series of places and in social interaction with milieus”.

o In this session, we will highlight the differences between ML and Narrative Enquiry. We will also discuss which type of sense making techniques can be used in both examples and how this can be used to deepen your understanding of the market.


The ease of creating your own AI experiment (even for nonprogrammers)

o In this session we will introduce the participants to the process of creating their own first AI experiment. The purpose of this exercise is to demystify the AI and ML environments and to give the participant an idea of the AI ecosystem.


Identifying AI problems within your organization

o To apply AI and ML algorithms successfully to a business problem requires the identification of problems and tasks that can be performed by AI. In this section we will define AI in a business context and delineate AI from closely related concepts such as automation and Big Data. Based on these definitions, we will provide guidelines on how to identify tasks that can be performed by AI and how to identify the requirements and resources to train and deploy an AI solution.


The most popular AI algorithms and their uses

o This session will provide you with a deeper understanding of the most used state-of-the-art AI algorithms, explain what type of problems they solve and highlight their respective strengths and weaknesses.


Comparison of the different tools and development platforms used for AI and ML

o In this session, you will be introduced to the different tools and development platforms used in the world of AI and ML. The features, advantages and disadvantages of the various platforms will be discussed so that managers will be able to make informed decisions on the viability of the tools for specific projects.

o You will be briefly introduced to other AI solutions such as:

  • Open-source editors and frameworks for building intelligent systems.
  • Platforms to build smarter ML/AI applications that are fast and scalable.
  • Open, enterprise-grade machine learning platforms
  • Services with easy to use, open templates for a variety of intermediate and advanced AI workloads.
  • Java Agent Development Framework and simplified multi-agent system development.
  • Platforms to allow you to run experiments and make better products with less trial and error.
  • Cloud based models and an inference engine to help in model selection.
  • Open-source computer vision: Libraries of programming functions aimed mainly at computer vision.


Robotics in business

o This module delves into the key elements of robotics as a transformative AI technology, with a focus on automating processes and tasks. Through rich case studies and faculty led videos that survey robots and autonomous vehicles, you’ll learn how robotics can benefit an organization. You’ll have the opportunity, once more, to submit ideas regarding the potential for robotics to be deployed in a business context of your choice.


Ethical Considerations involving AI in Business and the society: 

o In this module, you’ll see examples of other kinds of AI as well as return to collective intelligence and the human-machine relationship. Here you’ll also consider the impact of AI on jobs, and the ethical and social implications of AI integration. You’ll be tasked with anticipating and planning for the risks and considerations that may apply to integrating AI in a business context of your choice.


The future of artificial intelligence

o This module will allow you to imagine the future of AI and its potential use in your organization. Using what you have learned in this course, you’ll create a business roadmap for the strategic implementation of AI and collective intelligence into an organization of your choice.


Course Textbook

Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence

Jon Krohn

Beyleveld Grant

Bassens Aglaé


Feedback Given to Participants in Response to Assessed Work 

  • Individual written feedback on coursework
  • Feedback discussed as part of a tutorial
  • Individual feedback on request
  • Model answers 


Developmental Feedback Generated Through Teaching Activities

  • Feedback is given at presentations and during tutorial sessions
  • Dialogue between participants and staff in tutorials and lectures



The course grade will be based on a final project presented by the participant and graded by the instructor. Participants much achieve a passing grade of 70% or more to be awarded a certificate of completion of the course.