Course Description: 

Generative AI for Beginners" is an introductory course that provides a comprehensive overview of generative artificial intelligence (AI) for learners with little to no prior experience in the field. This course will cover the fundamental concepts, techniques, and applications of generative AI, equipping students with the knowledge and skills to start exploring and experimenting with this transformative technology.

This course provides an accessible introduction to the world of Generative AI, designed for beginners with little to no prior experience in artificial intelligence. Participants will explore the foundational concepts of AI and machine learning, focusing specifically on generative models like GPT, GANs, and VAEs. Through a mix of theoretical lessons and practical exercises, students will learn how these models are trained, how they generate new data, and how they can be applied across various domains, including text, image, and music generation. By the end of the course, learners will have a solid understanding of Generative AI principles, practical skills in using AI tools, and insights into ethical considerations surrounding AI-generated content.

 Course Overview:

1. Introduction to AI and Machine Learning:

• What is AI? Understanding the basics of Artificial Intelligence and its history.
• Machine Learning Fundamentals: A gentle introduction to the principles of machine learning, including supervised, unsupervised, and reinforcement learning.
• Key Concepts and Terminology: Understanding important terms such as algorithms, models, data, training, and inference.

2. Generative Models Explained:

• What is Generative AI? Exploring the concept of generative models that create new content, such as text, images, or music.
• Types of Generative Models:
  • Generative Adversarial Networks (GANs): How GANs work, including the roles of the generator and discriminator.
  • Variational Autoencoders (VAEs): Understanding VAEs and their applications.
  • Transformers and GPT Models: Introduction to the architecture behind models like GPT (Generative Pre-trained Transformer) and their capabilities in generating human-like text.

3. Practical Applications of Generative AI:

• Text Generation: Hands-on experience with AI models that generate text, such as GPT. Learn how to use pre-built models for tasks like creative writing, chatbots, and summarization.
• Image Generation: Explore tools like GANs to create realistic images from scratch or enhance existing images.
• Music and Audio Generation: Discover how AI can compose music, generate sound effects, or even create new audio tracks.

4. Tools and Platforms:

• AI Development Platforms: Introduction to popular platforms and tools like TensorFlow, PyTorch, and OpenAI's API.
• Working with Pre-trained Models: How to leverage existing models without needing to train your own from scratch.
• Basic Programming Skills: Optional introduction to Python, focused on writing simple code to interact with generative models.

5. Ethical Considerations:

• Bias in AI: Understanding how bias can be introduced in generative models and ways to mitigate it.
• Ethical Use of AI: Discussion on the responsible use of AI, including issues related to misinformation, deepfakes, and content ownership.
• Regulatory and Legal Implications: Overview of current regulations and laws concerning AI-generated content.

6. Project-Based Learning:

• Capstone Project: A final project where students apply their knowledge to create a generative AI application of their choice. This could be a text generation tool, a simple image generator, or another creative application.
• Peer Review and Feedback: Collaborative sessions where students share their projects and receive constructive feedback.

7. Course Resources and Support:

• Learning Materials: Access to a range of resources including video lectures, readings, and coding examples.
• Community Support: Join a community of learners to share ideas, ask questions, and collaborate on projects.
• Instructor Guidance: Regular office hours and Q&A sessions with the instructor to help with any challenges.

Target Audience:

This course is ideal for:

• Individuals new to AI who want to understand the basics of generative models.
• Hobbyists and tech enthusiasts looking to experiment with AI tools.
• Students or professionals in non-technical fields interested in how AI can be applied to creative tasks.
• Anyone curious about the potential and limitations of AI-generated content.

Prerequisites:

• No prior knowledge of AI or programming is required.
• A basic understanding of computers and internet usage is recommended.

Duration:

• Course Length: 6-8 weeks, with 4-6 hours of study per week.
• Self-Paced Learning: Flexible learning schedule with deadlines for assignments and projects.

Outcomes:

By the end of this course, students will:

• Have a foundational understanding of generative AI and its various applications.
• Be able to use pre-trained generative models to create text, images, or music.
• Understand the ethical implications and challenges associated with AI-generated content.
• Be prepared to further explore advanced topics in AI or apply their knowledge in creative projects.

Course Objectives:

- Provide a comprehensive introduction to the fundamental concepts and techniques of generative artificial intelligence

- Equip students with the knowledge and practical skills to start experimenting with and applying generative AI in various domains

- Explore the diverse applications of generative AI, including content creation, data augmentation, and creative expression

- Foster an understanding of the ethical considerations and potential societal impacts of generative AI

This course provides a comprehensive yet accessible introduction to the exciting field of Generative AI, empowering learners to harness the creative power of AI in their own work. The course will start on 25 February 2025.