Introduction to Generative AI

Artificial Intelligence (AI)

COURSE OVERVIEW


This introductory course on generative artificial intelligence (AI) provides a comprehensive overview of the foundational principles and techniques that empower machines to produce complex outputs, including text, images, video, and music. Students will examine the history and evolution of generative AI, tracing key milestones and landmark models that have shaped the field. The course begins with classical approaches—such as expert systems, genetic algorithms, Markov models, and constraint satisfaction problems—before advancing to modern generative techniques, including neural networks, autoencoders, generative adversarial networks (GANs), diffusion models, and Transformers. In addition to core models, students will be introduced to advanced topics such as prompt engineering, retrieval-augmented generation (RAG), and generative agents—autonomous AI systems capable of decision-making and goal-oriented behavior in dynamic environments. Ethical considerations and the societal impacts of generative AI, including concerns around bias, fairness, misinformation, and privacy, will be woven throughout the curriculum to foster responsible innovation.


Assessment will include both research and project-based work, with students expected to design and implement a generative AI application from concept to deployment. By the end of the course, students will have a strong foundational understanding of generative AI and practical skills to creatively and ethically apply these technologies across diverse domains.


WHAT YOU'LL LEARN

·      Learn the basic structure and paradigms of modern Generative AI

·      What Variational Autoencoders (VAEs) are and how they work

·      What Generative Adversarial Networks (GANs) are and what they are used for

·      What the applications of the Neural Network models are

 

PREREQUISITES

·      Some programming experience is useful but not required.

·      Basic understanding of Linux and cloud environments is recommended.


Duration: 15 Days / 120 Hours

Delivery Method: Classroom-based, Virtual Instructor Led Training

CONCEPTS CONTENT


Week 1: Foundations of Generative AI

·      Learn what generative AI is and how it has evolved from early AI to the large language models used today

·      Understand how these models work in applications by learning about model architectures and the training process

·      Get an overview of major foundation models like ChatGPT and Hugging Face, highlighting their capabilities and limitations

·      Explore the generative AI landscape, comparing options like open-source models, local models, and cloud APIs


Week 2: Interacting with Models

·      Learn the fundamentals of prompt engineering to interact effectively with generative AI models

·      Understand the concept of few-shot prompting and practice basic prompting techniques using context and examples

·      Learn methods for improving prompts through personas, detailed instructions, and iteration based on feedback

·      Explore more advanced skills like breaking down tasks, chaining prompts, and other techniques to overcome context limitations


Week 3: Building Robust Generative AI Systems

·      Explore different types of generative AI applications, including API-based, embedded model, and multi-model systems

·      Learn the fundamentals of building robust applications using techniques like Retrieval Augmented Generation (RAG) to improve context

·      Gain hands-on experience testing an application locally and deploying it on the cloud using tools like Azure


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