Course curriculum

Machine Learning (ML) – a subset of Artificial Intelligence behind a series of important technological breakthroughs (automated translation systems, medical image analysis, virtual assistants) – is profoundly transforming “traditional” business models & processes across sectors, by optimizing the processing of massive data volumes and automating core tasks. The fast-paced expansion of ML uses, especially in data-driven industries (financial services, health care, retail), is rapidly pushing forward the demand for skilled ICT workers in the EU. Whereas the demand for ML skills is steadily growing, employers are facing a shortfall of suitable candidates, which is leaving thousands of positions unfilled (an estimated 769,000), threatening productivity, efficiency & future growth. The strengthening of both initial and continuous VET provision in the field is therefore essential so that the European ICT workforce can acquire and develop the mix of ML technical (data modeling, software engineering), non-technical (governance, business management), and meta (sense of initiative and entrepreneurship) skills required to deliver and support the uptake of tailor-made ML enabled solutions in the market. The MACHINA VOOC is an up-to-date, self-guided, modular course for ICT professionals, who need to improve their skills, knowledge, and competencies in Machine Learning (ML) methods and practical applications. ICT professionals and anyone who follows this course will acquire and develop ML technical and non-technical skills required to respond to modern workplace requirements and succeed in a competitive labor market.

    1. Syllabus

    1. Welcome Activity

    1. Introduction to Learning Unit 1

    2. Lesson 1: Introduction to ML

    3. Lesson 1: Presentation

    4. Lesson 1: Lecture Notes

    5. Lesson 2: Where to Apply ML

    6. Lesson 2: Presentation

    7. Lesson 2: Lecture Notes

    8. Lesson 3: Machine Learning and Data Processing

    9. Lesson 3: Presentation

    10. Lesson 3: Lecture Notes

    11. Lesson 4: Example ML Applications

    12. Lesson 4: Presentation

    13. Lesson 4: Lecture Notes

    14. Questions and Answers

    15. Case Studies

    16. Practical Exercises

    17. Multiple Choice Questions

    1. Introduction to Learning Unit 2

    2. Lesson 1: Set, Functions, Relations

    3. Lesson 1: Presentation

    4. Lesson 1: Lecture Notes

    5. Lesson 2: Linear Algebra

    6. Lesson 2: Presentation

    7. Lesson 2: Lecture Notes

    8. Lesson 3: Probability Theory

    9. Lesson 3: Presentation

    10. Lesson 3: Lecture Notes

    11. Lesson 4: Statistics

    12. Lesson 4: Presentation

    13. Lesson 4: Lecture Notes

    14. Lesson 5: Computation Theory

    15. Lesson 5: Presentation

    16. Questions and Answers

    17. Case Studies

    18. Practical Exercises

    19. Multiple Choice Questions

    1. Introduction to Learning Unit 3

    2. Lesson 1: Machine Learning by Linear Models

    3. Lesson 1: Presentation

    4. Lesson 1: Lecture Notes

    5. Lesson 2: Supervised Learning Algorithms

    6. Lesson 2: Presentation

    7. Lesson 2: Lecture Notes

    8. Lesson 3: Unsupervised Learning Algorithms

    9. Lesson 3: Presentation

    10. Lesson 4: Semi Supervised Learning

    11. Lesson 4: Presentation

    12. Lesson 5: Programming Languages and Frameworks for Machine Learning Algorithms

    13. Lesson 5: Presentation

    14. Lesson 6: Best Practices for ML

    15. Lesson 6: Presentation

    16. Questions and Answers

    17. Case Studies

    18. Practical Exercises

    19. Multiple Choice Questions

    1. Introduction to Learning Unit 4

    2. Lesson 1: Multiplayer Perceptron (MLP)

    3. Lesson 1: Presentation

    4. Lesson 1: Lecture Notes

    5. Lesson 2: Convolutional Neural Networks (CNN)

    6. Lesson 2: Presentation

    7. Lesson 2: Lecture Notes

    8. Lesson 3: Recurrent Neural Networks (RNN)

    9. Lesson 3: Presentation

    10. Lesson 3: Lecture Notes

    11. Lesson 4: Autoencoders (AE), Restricted Boltzmann Machines (RBM)

    12. Lesson 4: Presentation

    13. Lesson 4: Lecture Notes

    14. Questions and Answers

    15. Case Studies

    16. Practical Exercises

    17. Multiple Choice Questions

About this course

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  • 115 lessons
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About the project

The project forms a Strategic Partnership to address existing occupational skills needs & mismatches. MACHINA goals are to: Design a joint VET curriculum in ML, to empower ICT workers with sought-after technical, non-technical, meta (soft) skills. Introduce flexible training delivery methods and innovative open-access pedagogical resources to support VET provision and ML skills acquisition. Foster the recognition and integration of ML skills requirements into sectoral competence frameworks & certification schemes. You can visit the project's website for more information.

Getting around for students

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Course Highlights

  • 27 sets Power Pointlaptop

  • 27 sets Lecture Notes

  • 12 Case Studies

  • 76 Questions & Answers

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 that 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 MACHINA 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]

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 (MACHINA project under the Erasmus+ Programme of the European Union")

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.

The MACHINA Partnership