Understanding Generative AI - Main Principles, Methods, and Applications
Understanding Generative AI - Main Principles, Methods, and Applications
Understanding Generative AI: Main Principles, Methods, and Applications is a weekend course that provides a comprehensive introduction to the underlying key concepts in Generative AI. Participants will explore the core principles behind of Large Language Models (LLMs) and image generative models such as variational autoencoders (VAEs), and diffusion models. The course combines engaging lectures, hands-on exercises, and real-world case studies to delve into foundations and practical applications of generative AI.
The course aims to give participants a solid grasp of the main principles behind generative models and enable them to implement and experiment with these models in practice. It highlights how generative techniques are revolutionising industries—from creative arts and design to healthcare and marketing—and encourages participants to explore how they can apply these tools within their own fields.
Designed for professionals or individuals interested in exploring this exciting area, the course is ideal for data scientists, AI practitioners, business leaders, entrepreneurs, and graduate students. A BSc-level mathematical background, with comfort in basic linear algebra, probability, and statistics, and basic programming skills is recommended. Pre-course readings and tutorials will be provided to establish a strong conceptual baseline.
Key Information
Dates: 17-19 October 2025
Duration: 12 hours of teaching, delivered over Friday-Sunday
Application Deadline: 19 September 2025
Location: Warwick Conference Centres at the University of Warwick campus in Coventry, CV4 7AL
Course Fee: £2100
Syllabus
Delegates on the course can expect to explore the following key topics:
- Foundations of Generative AI: What is generative AI?
- Overview of generative vs. discriminative models. Key concepts and terminology
- Essential Foundational Concepts and Challenges in Generative Modelling
- Fundamentals of Large Language Models
- Advanced Techniques and Emerging Models in Generative AI for Images/Videos: Diffusion Models, text-to-image models
- Applications and Industry Case Studies
- Hands-On Workshop (Coding, running of Pretrained DGMs, etc.)
Learning Outcomes
By the end of the module, students should be able to:
- Understand the core concepts of generative models, including their role and importance in modern AI applications
- Explore state-of-the-art neural network architectures used in large language models (LLMs), with a focus on Transformers
- Explain how large-scale generative models are trained and how they are fine-tuned for specific downstream tasks
- Understand reasoning and test-time computation in models such as ChatGPT, including how these models generate coherent and context-aware responses
- Apply prompt engineering techniques to improve the quality and relevance of outputs generated by LLMs
- Grasp the fundamentals of cutting-edge image generative models, particularly diffusion models, including their underlying principles and architecture
- Generate images using text prompts, understanding how text-to-image models interpret and transform prompts into visual content
- Implement basic generative AI solutions, engaging in hands-on exercises using Python notebooks and pretrained models (for both text and image generation)
- Gain practical experience in running scripts and interacting with popular Generative AI tools, enabling immediate application in research or professional contexts
- Evaluate real-world use cases of generative AI across industries (e.g., marketing, healthcare, design, customer service), and begin to propose innovative applications or solutions relevant to their own domains
*All delegates will receive a University of Warwick certificate of attendance