Decoding AI: A Deep Dive into AI Models and Predictions explores the significance of large datasets, demystifies generative artificial intelligence (AI), and challenges common media myths about AI. By defining key terms and exploring how systems “learn” from data, you will gain a baseline understanding of how AI works. Work to understand different critiques of AI narratives, learn to navigate conversations with precision, discern conflicts of interest, and appreciate the multidisciplinary expertise needed to shape AI's impact on society. This course provides you with the strategies and frameworks to engage in better conversation about the role of AI in your work and beyond.
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This is the third course in Understanding Data: Navigating Statistics, Science, and AI Specialization, in which you’ll gain a core foundation for statistical and data literacy and gain an understanding of the data we encounter in our everyday lives.
This course is part of the Understanding Data: Navigating Statistics, Science, and AI Specialization.
What you'll learn
- Learn key concepts and terminology in artificial intelligence (AI), including machine learning, generative AI, and deep learning
- Learn the core components of machine learning systems, including data, models, and evaluation techniques
- Recognize why AI systems can fail and identify the kinds of work required to make useful technology
- Identify common pitfalls in conversations about AI and recognize conflicts of interest when interpreting claims about AI systems
Syllabus
Welcome, Introduction and What Does "Artificial Intelligence" Really Mean?
Module 1: How Do Machine Learning Systems Work?
Module 2: The Limits of Data and Prediction
Module 3: How to Have Better Conversations About AI
