Leveraging AI Models for Engineers (5 Hours)

Instructor: Dr. Yasin Salimibeni Duration: 5 hours This course provides practical insights and tools to integrate artificial intelligence (AI) into your engineering workflows. We'll start with foundational concepts in AI, machine learning (ML), and large language models (LLMs), exploring how these can enhance your work. Throughout, we’ll cover the strengths and limitations of AI, including practical applications like document retrieval, error detection, and knowledge processing, with a focus on responsible AI use in engineering. By the end, you’ll be ready to use AI tools effectively, increasing both efficiency and innovation. Chapter 1: Foundations of AI and Data Science - Introduction to AI & ML: Learn the basics of AI, machine learning, and how they differ. - The Role of Data: Explore data’s importance in AI, including types and preparation. - Generative AI & LLMs: Understand AI’s content generation with examples like ChatGPT, effective prompting, and use cases across industries. - Simplified Key Terms: Break down jargon like neural networks, tokens, and temperature. - Machine Learning Basics: Discover how machine learning leverages data, and why data literacy matters in every job. - Responsible AI Use: Gain best practices for security and responsible AI applications. Chapter 2: Applied AI for Engineering Building on the fundamentals, we apply AI concepts to engineering: - Document Processing & Retrieval: Learn to automate document verification and retrieval for optimized workflows. - LLMs and RAG: Discover advanced search applications in technical documentation using retrieval-augmented generation. - Optimizing AI Outputs: Explore tokens, temperature, and tuning for tailored outputs. - AI in Complex Tasks: See how AI assists with calculations and simulations, enhancing decision-making and data-driven insights. - Best Practices: Understand strategies for verifying AI outputs, collaborating effectively with AI, and recognizing potential risks.