Business Applications of Artificial Intelligence

In this course students will get the foundational understanding of where we are today with Artificial Intelligence (AI) both from a technology standpoint and from a business standpoint. AI being defined as a collection of technics inspired by the goal of understanding and implementing intelligent behaviors. Key AI technologies will be covered like machine learning, deep learning and computer vision, natural language processing and a strong focus will be on the business implications of AI.

Outcomes Main learning outcomes of this course include an understanding of core technologies that make up an AI business application and a practical experience and usage of a development platform and supporting services to build an AI solution. A large part of the course will focus on practical hands-on experience through a series of guided labs and tutorials with two different approaches:

1. The clickers approach guided towards business persons with minimal coding experience
2. The coders approach Throughout the course, students will alternatively learn to customize out-of-the-box AI services and develop and implement their own AI services and integrate those in a business application.

After having taken this course, participants will be able to/are expected to know or understand (knowledge-based outcomes)

• Understand the core AI technologies
• Understand and describe learning algorithms and their usage in industry use-cases
• Identify and describe constraint satisfaction problems
• Understand the technics and use-cases of NLP
• Understand the different technics of computer vision
• Use a sophisticated AI platform (IBM Watson) More specifically, participants should be able to (skill- and competency-based outcomes)
• Develop artifacts to be integrated in Business Applications for instance chat-bots, visual recognition systems
• Design an application of AI

---

Environment setup

---

Resources

There are a lot of resources available to go deeper into AI and related technologies. Some are listed in the Resources.md

---

Evaluation Process

Your personal grade will be a combination of:

1. Individual Work which is the grade you'll get for the Quiz on Day 4 (April 19th) and
2. Group Work which is the combined grade of your team effort to:

• Think about,
• Describe,
• Mockup,
• Present and AI use case

The group work is a Hackathon-style work throughout the entire duration of the workshop (more or less 1.5 month). Presentation of projects will take place on April 19th.

---

Agenda

Day one

• Introduction

  • Agenda Presentation & Evaluation Process

• Artificial Intelligence Fundamentals

  • What is AI?
  • Machine Learning
  • Industry Use Cases

• IBM Watson AI Platform (Services & Tools) & IBM Watson Machine Learning

• Hands-on Labs – Machine Learning 101  « The Clickers way » here - This Lab is going to walk you thru the setting of your environment, instantiation of the IBM Watson Studio service and basic data exploration, data visualization and data refinery tasks. You don't need to complete the entire data refinery part

• IBM Watson Machine Learning

• (Optional) Hands-on Labs – Machine Learning 101  « The Clickers way » here *This lab is going to walk you through the creation of your first Machine Learning model. It will predict the likelihood of a customer to churn for a Telco company

• Computer Vision

• Hands on Labs – Visual Recognition using the IBM Watson Visual Recognition service. This is the clickers approach available:

  • Getting Started with Visual Recognition Labs/DayOneLabs-Part2/Lab1-UsingVisualRecognitionWithUI/README.md
  • Create your custom classifier Labs/DayOneLabs-Part2/Lab2-CreateCustomClassifierWithUI/README.md

  Note: the following labs are optional but they might be useful for your Hackathon depending on what you to implement. You have plenty of time to work on those and get inspired before the submission date of your project.

  • Use Visual Recognition APIs Labs/DayOneLabs-Part2/Lab3-UsingVisualRecognitionAPIsWithCommandLine/README.md
  • Create and Retrain custom classifier with APIs Labs/DayOneLabs-Part2/Lab4-CreateAndRetrainCustomClassifierWithAPIs/README.md

• Node Red

  • Labs/DayOneLabs-Part2/Lab5-IntegrateVisualRecognitionInYourApplicationWithNode-Red/README.md

---

Day Two

• Deep Learning fundamentals
• Hands on Labs - Deep Learning for image recognition. This lab is going to walk you through the process of building Neural Networks to recognize hand written digits in different ways:
  1. Using Visual Recognition Service here
  2. Using a CNN here
  3. Building a NN in a Jupyter Notebook here
  4. Using the NN Builder from Watson Studio here

Day Three

This section covers the Natural Language Processing section of the labs. Labs will walk you through the usage of:

• IBM Watson Natural Language Classifier here
• The process of creating a chatbot using IBM Watson Assistant

---