This document describes SkyRadar's 2 years course in A.I, including elements of data science and connected sciences for undergraduate courses (Bachelor Programs).

The present document describes the structure of SkyRadar's learning material teaching in  A.I, including data science, learning machines and connected techniques.

The 2 year course is designed as an accompanying guidance and documentation. We suggest to apply it together with SkyRadar's practical experiment sets for Artificial Intelligence.

The goals of the resulting product are the following:

  • Introduction into Artificial Intelligence in a way, which is compatible with their assumed knowledge of Algorithmics, programming and mathematics, among others.
  • Offer a learning curve founded on pedagogical “learning paths” and learning strategies.
  • Stay coherent with the study of A.I. and data science as expected in university education.
  • Be ambitious, offer valorization of the topics and present the subject in attractive ways so to eventually attract young students and enlighten potential vocations.


Building school textbooks and school learning material isn’t an easy task and requires a high quality with respect to the subject, style and the embedding pedagogical concept.

The topics are clearly presented with crystal clear content. No ambiguous or incorrect data is presented. Besides, the content has been clearly optimized for education. It does not consist of “Wikipedia-style” essays but of  smart paths to knowledge and competence. 

Special attention is given to exercises and solutions. The material delivered is primarily exercise-focused.  

Overview of Deliveries

  • Two books for students totaling 40 hours per year lessons. Delivered on various formats ( pdf, html);
  • A teacher version to help them challenging the pupils. Delivered in various formats (pdf, html);
  • A teacher book to guide the teachers along with the lessons and help them build their own learning material;
  • A kit for practical work so that classrooms can create 20 different projects in the field of A.I , data science, learning machines. The kit comprises various items such as PowerPoint presentations.

Learning Material content

Phase I

Introduction to AI

  • Introduction to AI and setting up the context of the curriculum
  • Introduction to Machine learning

To identify and appreciate Artificial Intelligence techniques in various domains of Information technology.

An overview of Artificial Intelligence to understand how Machine Learning provides the foundation for AI

To imagine, examine and reflect on the skills required for the futuristic opportunities.

We emphasize the difficult definition(s) of Artificial Intelligence … what is it exactly? Some history will also be detailed, especially Norbert Wiener and the link of A.I with cybernetics. We also introduce self-organization as a possible follow-up of Artificial Intelligence.

AI vs nature imitation vs mechanical “toys”

AI is more than “just” algorithms or finite state machines.

We also reflect on the possible careers and challenges of A.I and the future works and projects that could be achieved worldwide.


Exposure to AI using Apps


  • Face and Emotion Recognition
  • Speech Authentication
  • Text Analytics
  • Language Understanding        
  • Video Indexer
  • Cortana-an Intelligent Assistant
  • Sketch2Code

Exposure to AI apps that imitates patterns and actions of humans. Learners would understand the application of Artificial Intelligence in their daily lives.

Sketch2code is a Microsoft AI research app

Cortana is a virtual assistant created by Microsoft for Windows 10

We explain how these apps are built. Also mentioning previous apps written in LISP for example like ELIZA as an ancestor of Cortana.

We advise to also use the SkyRadar Practical Exercise Sets.

Detailing techniques used for face auth, emotions detections etc.

We would also introduce actual robotic “chatbots”, e.g., conversational androids

These are very striking examples of what AI can achieve nowadays when coupled with mechatronics.

AI Algorithms & Problem solving

  • A* Algorithm
  • AO* Algorithm
  • Searching Algorithm
  • Hashing Algorithm
  • Identify a problem that you would like to solve
  • How do you identify and define a problem, dealing with generating and shortlisting ideas
  • Defining the boundary of the problem
  • How will data be accessed, managed and analyzed

Learn problem scoping and the ways to identifying the goals set for an AI project.                 

Design thinking, problem identification, generating and working with data, representation and privacy aspects

Understanding the process of identifying problems, generating ideas and design a computational solution to a problem described in natural language, express the solution in an algorithmic way, and convert the algorithm into the solution         

Learners would understand various AI searching algorithms

Searching algorithms are introduced by real-case examples like the Ant colonization algorithms etc.

The learning material for that section tries to stimulate the imagination of students so to raise ideas about some real search problems. This would be linked to exercises.

Understanding Data & Data Sets

  • Data and its Utility
  • Different types of data-sets used in Machine Learning
  • Image         Processing
  • Sentiment Analysis
  • Natural Language Processing
  • Video Processing
  • Speech Recognition
  • IOT        

Identify requirements of data and data sets and find reliable sources to obtain relevant data.

Why data-sets matter in Machine Learning

Learners would understand the different types of data-sets.

What is data, how was data used before computers were invented. Different types of data. Why is AI so inextricably linked to data. What is a data scientist? How is data used for different categories of problems.

We build on the definition of Artificial intelligence data by Microsoft

Data, as the foundation for all advanced analytics and machine learning, is one of the most strategic assets a company can have. Advances in data science and prebuilt AI services put that world within reach for every organization on the planet. At Microsoft, we have made AI an integral part of our own digital transformation

AI Learning Techniques

  • Supervised Learning
  • Unsupervised Learning
  • Neural Networks
  • Back propagation

Understand working with teams and team roles, enhancing effectiveness and conflict resolution

Understand and appreciate the concept of Neural Network

Understand the importance of mathematical tools for improving the accuracy of predictions in machine learning

Students in Phase I (typically Year 1 or Year 3 bachelor students) have the required background to understand AI mathematics. It is possible also to explain Neural Networks, vector machines, classification.

Basics of Cloud based application

  • What is a cloud?
  • Applications of cloud by the organizations
  • Understanding of issues around management of cloud        
  • Major cloud platforms and applications

Articulate the main concepts, key technologies, strengths, limitations and possible applications for state-of-the-art cloud computing.

Understanding the advantages and disadvantages of cloud based applications

History of cloud / akamai techs/ what is distributed computing .We must emphasize why cloud computing has big advantages over “traditional” computing techniques as well. Describes “modern” cloud and execution of containers / runtimes / virtual machines in a distributed system.

AI Ethics

  • Discussing about the possible bias in data collection
  • Discussing about the implications of AI technology

To understand and reflect on the ethical issues around AI.

To gain awareness around AI bias and AI access.

This “meta-techno” subject. We will present a comprehensive view of AI and ethics using our business knowledge of what is produced in terms of population controls or robotics.

Project Work

  • Solve a basic problem applying the AI concepts learnt in the theory.


How to represent their project effectively.

To apply understanding of AI to project creation.

How to Synthesize technical and creative knowledge while creating their projects.

We will propose ideas for creating up to 20 projects. And use the SkyRadar AI exercises.


Phase II


Introduction to advanced computing environments.

  • Evolution in computing environments and models.                
  • Fundamentals of Artificial Intelligence, Machine Learning, Data Analysis.                                
  • Understanding use of AI, ML and DA in various sectors like Banking, Education, Healthcare, manufacturing etc.

We present in more detailed terms AI , Machine Learning and Data Analysis. We will present concrete uses cases.

AI techniques, algorithms and real time systems                        

  • Search techniques- informed, uninformed & decision making
  • Knowledge base and reasoning techniques        
  • Learning techniques- supervised and unsupervised.        
  • Fuzzy logic & Expert systems.

Definition of  real-time system/ RTOS and example of such systems in industry.

Decision algorithms. Brain-based learning technique/Bayesian.

Definition of fuzzy logic. Applications of AI + RTOS + Learning Systems in military applications ( Intelligent tanks , etc… )

Data analysis and techniques

  • Understanding mathematical and statistical models
  • Standard libraries apophenia, gsl etc.
  • Data sensitivity analysis concept
  • Distribution and test.
  • Operation research

Statistical understanding of data analysis is challenging but we make it accessible. We made sure to be consistent with their math knowledge. 

Machine learning                        

  • Learning types                        
  • Concept of RPA.                        
  • ChatBOT        
  • Introduction To Python & R language                
  • Artificial neural network

We use python codes to demonstrate neural networks and how to build a chatbot in python.


School books for students

The book is the main reference for students. It contains all the data needed to apprehend the lessons. There are two books. One for grade XI and one for grade XII.

Approximate volume  250 words/page and 200 pages per book.

50,000 words/ book in phase I.

50,000 words/ book in phase II.

The books are provided in pdf and HTML format. The can be used for signed up students of the university. University Intranet is allowed. Free distribution in the internet is explicitly forbidden and will be considered a violation of the contract.

Guide Books for teachers

This book contains additional references for teachers and also hints to build their own educational material.

The book offers non-linear paths and references bricks to

  • Stimulate interest and curiosity;
  • Reveal eventually talents inside the audience;
  • Form strategies for learning;
  • Help teachers to combine exercises, video training and collective projects in lessons.

Approximate volume  250 words/page and 80 pages per book.

20,000 words/ book phase I.

20,000 words/ book phase II.

The books are provided in pdf and HTML format. The can be used for signed up students of the university. University Intranet is allowed. Free distribution in the internet is explicitly forbidden and will be considered a violation of the contract.

Answer book (teacher)

This book contains a list of exercises and their solutions, their difficulties range from 0 (simple) to 4 (very hard). Using exercises is encouraged in the regular practice of teaching introduction to AI. In fact a lesson without exercises have no meaning and the answer book is thus the most important book of the delivery. . Note that the exercises ( without their answer of course ) are also listed in the student books.

Approximate volume will be 250 words/page and 400 pages per book.

100,000 words/ book phase I and phase II.

The books are provided in pdf and HTML format. The can be used for signed up students of the university. University Intranet is allowed. Free distribution in the internet is explicitly forbidden and will be considered a violation of the contract.

Kit for practical work

That kit will contain various educational materials so that classrooms can work on concrete projects involving AI and the topics connected to AI as taught in the lessons.

The kit comprises various items such as PowerPoint presentations or link lists to resources for AI on the weblike  IBM’s watson or tensorflow etc. 

This Course builds on SkyRadar's training Hardware

The course in conceived to work with SkyRadars practical teaching contents.

The SkyRadar Radar-based courses are not part of this set of deliverables.

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