Basic concepts on AIGC
  • About the course materials
  • General Course Format and Strategies
  • Introduction
  • Foundations for AIGC
    • Computers and content generation
    • A brief introduction to AI
      • What AI is?
      • What ML is?
      • What DL is?
      • Discriminative AI vs. Generative AI
  • Generative AI
    • Introduction to Generative AI
      • Going deeper into Generative AI models
  • Deep Neural Networks and content generation
    • Image classification
    • Autoencoders
    • GAN: Generative Adversarial networks
    • Transformers
    • Diffusion models
      • Basic foundations of SD
  • Current image generation techniques
    • GANs
  • Current text generation techniques
    • Basic concepts in NLP in Large Language Models (LLMs)
    • How chatGPT works
  • Prompt engineering
    • Prompts for LLM
    • Prompts for image generators
  • Current AI generative tools
    • Image generation tools
      • DALL-E 2
      • Midjourney
        • More experiments with Midjourney
        • Composition and previous pictures
        • Remixing
      • Stable diffusion
        • Dreambooth
        • Fine-tuning stable diffusion
      • Other solutions
      • Good prompts, img2img, inpainting, outpainting, composition
      • A complete range on new possibilities
    • Text generation tools
      • OpenAI GPT
        • GPT is something really wide
      • ChatGPT
        • Getting the most from chatGPT
      • Other transformers: HuggingFace
      • Other solutions
      • Making the most of LLM
        • Basic possibilities
        • Emergent abilities of LLM
    • Video, 3D, sound, and more
    • Current landscape of cutting-edge AI generative tools
  • Use cases
    • Generating code
    • How to create good prompts for image generation
    • How to generate text of quality
      • Summarizing, rephrasing, thesaurus, translating, correcting, learning languages, etc.
      • Creating/solving exams and tests
  • Final topics
    • AI art?
    • Is it possible to detect AI generated content?
    • Plagiarism and copyright
    • Ethics and bias
    • AI generative tools and education
    • The potential impact of AI generative tools on the job market
  • Glossary
    • Glossary of terms
  • References
    • Main references
    • Additional material
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  • Teaching approach: blended learning
  • Teaching tools
  • Specific details on Introduction to content creation with AI generative tools

General Course Format and Strategies

Teaching approach: blended learning

  • Teaching materials: traditional content + videos

    • Ansynchronous 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 explanaitions, 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 sheduling if required

  • Assessment: Project-based assessment with mandatory online defense (syncrhonous/asynchronous based on number of students)

Teaching tools

  • Poliformat 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 UVP course of 7.5 ECTS

  • 100 estimated average student working hours (videos + material study + syncrhonous sessions)

  • Estimated student work: 8-16 hours/week based on student's final availability and objectives

  • Estimated duration of the course: 3 months (April, May, June)

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Last updated 2 years ago