STUDIES

AI for ICT professionals (English Edition)

Modality: Online
Duration: 150h
Date: May - December 2023
Method: Mainly practical, with many use cases
Price: 1500€ - Grants available!

In recent years, AI has become a hype term, omnipresent and of which, unfortunately, even professionals linked to the field of ICT are unaware of its foundations, capabilities and also limitations and problems. 

Providing professionals in the technological and ICT fields with the necessary foundations to understand the current mechanisms that support the new AI revolution is undoubtedly the main objective of this course.

Objectives and targets

This course is especially aimed at those with an interest in:

  • Know the fundamentals that underlie current AI solutions. Without a deep technical or mathematical depth, but with enough rigor to know its mechanisms.
  • Know the different approaches what it is called Artificial Intelligence, and provide the current landscape of possibilities and their applications
  • Address a practical approach to AI, focusing its study through practical analysis of the different problems that can now be solved thanks to the latest advances in the field.

The course does not require prior technical foundations in AI and is open to:

  • ICT professionals
  • Engineers or professionals working on technological sectors
  • Professionals with current knowledge on software development solutions but not familiar with current AI solutions (mainly focused on machine and deep learning)

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Metodology

  • Introduction to basic fundamental elements, skipping deep mathematical background, and with just some background in programming and software development
  • Mainly practical, with many use cases
  • End-to-end solutions and different use cases
  • A Data-centric approach as encouraged by Andrew Ng (https://datacentricai.org/) to approach the use of AI in problem solving
  • A Foundation Model approach (https://crfm.stanford.edu/, https://arxiv.org/pdf/2108.07258.pdf), where models will be presented with the required explanations to understand them, but with the main focus on using them to solve particular problems and future challenges
  • Use of state-of-the-art tools for solving all the stages in AI product analysis, development, and deployment:
    • Python and main libraries overview
    • AutoML tools: pycaret (low code ML), BigML (data-driven no-code solutions), etc.
    • Google Colaboratory, Jupyter Notebooks, VSCode + Copilot (and other possible AI assistants), etc.
    • HuggingFace and other open source foundation models providers
    • Deployment: REST API, dockers

What is Data-centric AI?

Data-centric AI is the discipline of systematically engineering the data used to build an AI system.

Train one model on a huge amount of data and adapt it to many applications. We call such a model a foundation model.

Online - Blended learning

  • Teaching materials: traditional content + videos
    • Asynchronous videos focusing on main concepts, skipping much more dynamic details, following standard recommendations: 7 min. max, main concepts, real teacher on screen, free use of any tool (standard template for slides), screen casts, other possibilities when required.
    • Text: Reading material with full explanations, references, links to additional material, and video slides.
  • Forums
  • Weekly synchronous (also recorded) sessions of the scheduled topics of the week: introduction to the topic, summarizing and a better way to approach to the work of the week.
  • Tutorial support, with online meeting scheduling if required.
  • Assessment: Project-based assessment with mandatory online defense (synchronous/asynchronous based on number of students).

Teaching tools

  • Poliformat UPV official LMS platform for main group creation and final evaluation.
  • media.upv.es for video hosting.
  • SPOOC format on edX Studio hosted in UPV -> Flexible LMS with a powerful authoring tool.
  • Teams for synchronous sessions.
  • Standard templates for slides and material.

Specific details on Introduction to content creation with AI generative tools

  • Formal CFP UPV course of 15 ECTS.
  • 300 estimated average student working hours (videos + material study + synchronous sessions).
  • Estimated student work: 8-16 hours/week based on student’s final availability and objectives.
  • Estimated duration of the course: 7 months (May, June, July, September, October, November, December).