Course curriculum

Artificial Intelligence (AI) is revolutionizing the way the economy and society function, by automating tasks & business processes, and managing workflows & critical data more effectively. The fastpaced development of AI technologies in diverse economic and social realities is exponentially augmenting the demand for ICT professionals with the right combination of AI technical, non-technical and transversal skills. Recent market surveys show that the demand for AI skills has almost tripled over the past 3 years and the number of relevant job postings is up by 119%. Employers, however, struggle to find candidates with the right skill mix. Further to demand, the gap is amplified by the shortage and inadequacy of relevant skills expected via Vocational Education and Training (VET) provision, given also that AI is currently a subject of ICT specialization mostly offered at the highest level of tertiary education. The ARIS VOOC is an up-to-date, self-standing, modular course for ICT professionals, who need to improve their skills, knowledge and competencies in AI technologies and practical applications. ICT professionals who follow this course will acquire and develop AI-related skills - along with problem-solving, managerial and customer-related (transversal) skills - required to respond to modern workplace requirements and succeed in a competitive labour market.

    1. Unit 1

    2. Unit 2

    3. Unit 3

    4. Unit 4

    1. Welcome Activity

    1. Introduction to Learning Unit 1

    2. L1.1: Scope of Artificial Intelligence - Theoretical Content

    3. L1.1: Presentation

    4. L1.1: Notes

    5. L1.1: Use Cases

    6. L1.1: Practical Exercises

    7. L1.1: Questions and Answers

    8. L1.1: Questionnaire

    9. L1.2: Problem Solving with Search Algorithms - Theoretical Content

    10. L1.2: Presentation

    11. L1.2: Notes

    12. L1.2: Use Cases

    13. L1.2: Practical Exercises

    14. L1.2: Questions and Answers

    15. L1.2: Questionnaire

    16. L1.3: Knowledge Representation - Theoretical Content

    17. L1.3: Presentation

    18. L1.3: Notes

    19. L1.3: Use Cases

    20. L1.3: Practical Exercises

    21. L1.3: Questions and Answers

    22. L1.3: Questionnaire

    23. L1.4: Machine Learning - Theoretical Content

    24. L1.4: Presentation

    25. L1.4: Notes

    26. L1.4: Use Cases

    27. L1.4: Practical Exercises

    28. L1.4: Questions and Answers

    29. L1.4: Questionnaire

    30. L1.5: Applications of Artificial Intelligence - Theoretical Content

    31. L1.5: Presentation

    32. L1.5: Notes

    33. L1.5: Use Cases

    34. L1.5: Practical Exercises

    35. L1.5: Questions and Answers

    36. L1.5: Questionnaire

    37. L1.6: Ethical Implcations of Artificial Intelligence - Theoretical Content

    38. L1.6: Presentation

    39. L1.6: Notes

    40. L1.6: Use Cases

    41. L1.6: Practical Exercises

    42. L1.6: Questions and Answers

    43. L1.6: Questionnaire

    44. L1: Final Assessment - Self Check 1

    45. L1: Final Assessment - Self Check 2

    1. Introduction to Learning Unit 2

    2. L2.1: Introduction to Machine Learning - Theoretical Content

    3. L2.1: Presentation

    4. L2.1: Notes

    5. L2.1: Use Cases

    6. L2.1: Practical Exercises

    7. L2.1: Questions and Answers

    8. L2.1: Questionnaire

    9. L2.2: Languages and Resources - Theoretical Content

    10. L2.2: Presentation

    11. L2.2: Notes

    12. L2.2: Use Cases

    13. L2.2: Practical Exercises

    14. L2.2: Questions and Answers

    15. L2.2: Questionnaire

    16. L2.3: Data Transformation and Visualisation - Theoretical Content

    17. L2.3: Presentation

    18. L2.3: Notes

    19. L2.3: Use Cases

    20. L2.3: Practical Exercises

    21. L2.3: Questions and Answers

    22. L2.3: Questionnaire

    23. L2.4: Linear Methods for Supervised Learning - Theoretical Content

    24. L2.4: Presentation

    25. L2.4: Notes

    26. L2.4: Use Cases

    27. L2.4: Practical Exercises

    28. L2.4: Questions and Answers

    29. L2.4: Questionnaire

    30. L2.5: Non Linear Methods for Supervised Learning - Theoretical Content

    31. L2.5: Presentation

    32. L2.5: Notes

    33. L2.5: Use Cases

    34. L2.5: Practical Exercises

    35. L2.5: Questions and Answers

    36. L2.5: Questionnaire

    37. L2.6: Unsupervised Learning - Theoretical Content

    38. L2.6: Presentation

    39. L2.6: Notes

    40. L2.6: Use Cases

    41. L2.6: Practical Exercises

    42. L2.6: Questions and Answers

    43. L2.6: Questionnaire

    44. L2. Final Assessment - Self Check 1

    45. L2. Final Assessment - Self Check 2

    1. Introduction to Learning Unit 3

    2. L3.1: Brain & Neural Networks - Theoretical Content

    3. L3.1: Presentation

    4. L3.1: Notes

    5. L3.1: Use Cases

    6. L3.1: Practical Exercises

    7. L3.1: Questions and Answers

    8. L3.1: Questionnaire

    9. L3.2: Simple Perceptions and Supervised Learning - Theoretical Content

    10. L3.2: Presentation

    11. L3.2: Notes

    12. L3.2: Use Cases

    13. L3.2: Practical Exercises

    14. L3.2: Questions and Answers

    15. L3.2: Questionnaire

    16. L3.3: Multiplayer Perceptrons and Keras - Theoretical Content

    17. L3.3: Presentation

    18. L3.3: Notes

    19. L3.3: Use Cases

    20. L3.3: Practical Exercises

    21. L3.3: Questions and Answers

    22. L3.3: Questionnaire

    23. L3.4: Deep Learning for Image Classification - Theoretical Content

    24. L3.4: Presentation

    25. L3.4: Notes

    26. L3.4: Use Cases

    27. L3.4: Practical Exercises

    28. L3.4: Questions and Answers

    29. L3.4: Questionnaire

    30. L3.5: Different CNN for Image Classification - Theoretical Content

    31. L3.5: Presentation

    32. L3.5: Notes

    33. L3.5: Use Cases

    34. L3.5: Practical Exercises

    35. L3.5: Questions and Answers

    36. L3.5: Questionnaire

    37. L3.6: Object Localization: YOLO_v3 model - Theoretical Content

    38. L3.6: Presentation

    39. L3.6: Notes

    40. L3.6: Use Cases

    41. L3.6: Practical Exercises

    42. L3.6: Questions and Answers

    43. L3.6: Questionnaire

    44. L3. Final Assessment - Self Check 1

    45. L3. Final Assessment - Self Check 2

    1. Introduction to Learning Unit 4

    2. L4.1: Word Embedding and Text Classification - Theoretical Content

    3. L4.1: Presentation

    4. L4.1: Notes

    5. L4.1: Use Cases

    6. L4.1: Practical Exercises

    7. L4.1: Questions and Answers

    8. L4.1: Questionnaire

    9. L4.2: Neural Networks for NLP and Libraries - Theoretical Content

    10. L4.2: Presentation

    11. L4.2: Notes

    12. L4.2: Use Cases

    13. L4.2: Practical Exercises

    14. L4.2: Questions and Answers

    15. L4.2: Questionnaire

    16. L4.3: New Approaches, Applications, Open Problems - Theoretical Content

    17. L4.3: Presentation

    18. L4.3: Notes

    19. L4.3: Use Cases

    20. L4.3: Practical Exercises

    21. L4.3: Questions and Answers

    22. L4.3: Questionnaire

    23. L4.4: Big data: Problems, Techniques, Hadhoop - Theoretical Content

    24. L4.4: Presentation

    25. L4.4: Notes

    26. L4.4: Use Cases

    27. L4.4: Practical Exercises

    28. L4.4: Questions and Answers

    29. L4.4: Questionnaire

    30. L4.5: Big Data: Hadhoop and Spark - Theoretical Content

    31. L4.5: Presentation

    32. L4.5: Notes

    33. L4.5: Use Cases

    34. L4.5: Practical Exercises

    35. L4.5: Questions and Answers

    36. L4.5: Questionnaire

    37. L4.6: Big Data: Analytics, Visualization, Applications - Theoretical Content

    38. L4.6: Presentation

    39. L4.6: Notes

    40. L4.6: Use Cases

    41. L4.6: Practical Exercises

    42. L4.6: Questions and Answers

    43. L4.6: Questionnaire

    44. L4. Final Assessment - Self Check 1

    45. L4. Final Assessment - Self Check 2

About this course

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About the project

ARIS is an EU-funded project that brings together 5 organizations from 5 EU Member States to form a Strategic Partnership with the mission to increase the relevance of Vocational Education and Training (VET) provision for ICT professionals to match their competencies with the latest developments in AI and promote employability & mobility within the broader ICT sector. To this end, the project will a) design a comprehensive and up-to-date training course in AI technologies and practical applications to empower ICT professionals with key transversal and AI-specific competencies, b) introduce modern training delivery methods and innovative open-access pedagogical resources, enabling learners to acquire and self-assess ΑΙ related skill, and c) facilitate the integration of AI skills requirements into sectoral classification schemes. For more information, please visit our website!

Getting around for students

First time on Thinkific? Click on the Learning Units tabs in the sidebar to access the course content and activities. On mobile, the navigation is located in the menu. To move forward after you’ve done an activity, scroll to the bottom of the page (be sure to check out what your fellow learners have said along the way) and click on “next page”. All activities must be attempted to reach 100% and complete the course! Nevertheless, the modular format of the course allows you to choose those materials and activities that best address your individual training needs and priorities.

FAQ

  • Will I get a Statement of Accomplishment after completing this course?

    Certificates of completion will be awarded to learners who have successfully completed all course activities, upon request. The certificates will act as evidence of professional development and skills acquisition; they do not represent an official degree.

  • What about timing? Can I take this self-paced?

    You can go at your own pace! Within any week of the course, you can look at the materials and take assessments whenever you have time available, regardless of your time zone. The course is completely online and you can access course materials and resources anytime via the web or your mobile device.

  • Do I need to take the course in a specific order?

    Whereas the course has a modular structure allowing learners to choose the modules and lessons they better address their needs and interests, it is highly recommended that learners take the course in order as each lesson builds upon the previous.

  • What are the tasks for this course?

    At the end and within the lessons, there are practical exercises, and quizzes that are intended to guide your understanding of what you have learned. Referring to indicative answers, together with input from other students (if available), you will self-mark your assignment work for correctness. However, the tasks are for understanding and development, not for marks. You are strongly encouraged to discuss your work with others before, during, and after the self-marking process.

  • Can I contact the facilitator?

    The ARIS VOOC is a self-guided course. Nonetheless, you can address your questions and queries regarding learning materials to the project staff at the following emails: [email protected] and [email protected]

Funded by Erasmus+

The European Commission support for the production of this publication does not constitute an endorsement of the contents which reflects the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

Terms of use

This content was modified under a Creative Commons 4.0 BY-SA license that allows for free use, distribution, and modification of materials, with reference to the creator (ARIS project under the Erasmus+ Programme of the European Union")

The ARIS Consortium