AI LITERACY
Instructional goals
This course aims to provide students with a foundational understanding of Artificial Intelligence (AI) and its applications across various domains. The course will also equip students with practical skills to leverage generative AI models for various tasks, including business use cases, problem-solving, and automation. The lab sessions will mainly focus on conversational AI, prompt engineering, evaluation of model outputs, multimodal capabilities, and task automation.
Intended learning outcomes
By the end of the course, participants will be able to: • Understand and compare generative AI models • Use prompt engineering to solve complex problems • Evaluate the reliability and quality of AI-generated outputs • Leverage multimodal AI models for diverse applications • Implement basic automation using AI tools
Course Contents
1. Introduction to AI 2. AI Techniques and Algorithms 3. AI Applications 4. Ethical and Societal Implications 5. AI and the Future 6. Conversational Models: Use Cases and Comparisons 7. Prompt Engineering for Conversational Models 8. Evaluating Model Outputs and Handling Errors 9. Multimodal Models for Complex Business Use Cases
Reference Books
Slides provided by the instructor.
Teaching Methods
The course consists of lectures complemented by practical lab sessions.
Assessment Method
A final promptathon
Thesis assignment criteria
The final work will be assigned (upon specific request to the instructor) to students who show a deep interest in the subject.
Week 1
Introduction to AI o Describe AI and its subfields (e.g., machine learning, deep learning, natural language processing, Generative AI). Explore the history and evolution of AI.
Week 2
AI Techniques and Algorithms o Understand key AI techniques and algorithms.
Week 3
AI Applications o Examine AI applications in various fields.
Week 4
Ethical and Societal Implications o Discuss the ethical challenges and explore the societal impact of AI (including regulations and governance).
Week 5
AI and the Future o Consider potential future developments in AI and its potential role in the future of society.
Week 6
Conversational Models: Use Cases and Comparisons o Overview of conversational AI models o Hands-on exploration of capabilities and limitations
Week 7
Prompt Engineering for Conversational Models o Techniques: zero-shot, few-shot, chain-of-thought reasoning, and persona-based prompting o Meta-Prompting o Applications to business use cases
Week 8
Evaluating Model Outputs and Handling Errors o Techniques for assessing response quality o Detecting and understanding hallucinations o Assisted research (e.g., using ChatGPT for search) o Exploring models that perform actions
Week 9
• Multimodal Models for Complex Business Use Cases o Introduction to multimodal models (e.g., Gemini, DALL-E) o Hands-on practice: combining text and visual inputs for a complex task o Business application: generating presentations, visual content, and analytics from multimodal inputs
Week 10
Q&A
Week 11
Q&A
Week 12
Q&A