Lecturer IV and Research Investigator
Decoding AI: A Deep Dive into AI Models and Predictions
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
4 Modules
8 Hours
2 hrs per module (approx.)
Rating
About Decoding AI: A Deep Dive into AI Models and Predictions
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.
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.
Skills You'll Gain
- AI Agents
- AI Innovation
- Artificial Intelligence
- Data Analysis
- Data Literacy
- Ethical AI
- Generative AI Agents
- Machine Learning
- Responsible AI
What You'll Earn
- Certificate of Completion:
- Certificates of completion acknowledge knowledge acquired upon completion of a non-credit course or program.
Welcome Message
Welcome to Decoding AI: A Deep Dive into AI Models and Predictions, part of the Understanding Data specialization. This course introduces learners to artificial intelligence, machine learning, and big data, focusing on how models “learn” and make predictions. Through real-world examples, you’ll gain the literacy to evaluate AI claims, understand data challenges, and engage in informed conversations about AI in your work and beyond. No prior experience is required.
This abbreviated syllabus description was created with the help of AI tools and reviewed by staff. The full syllabus is available to those who enroll in the course.
Course Schedule
Module 1: What Does "Artificial Intelligence" Really Mean?
- Video: Welcome to What Does "Artificial Intelligence" Really Mean?
- Reading: Meet Your Instructor
- Reading: Course Syllabus
- Discussion Prompt: Meet Your Fellow Global Learners
- Reading: Help Us Learn About You
- Video: How Did We Get Here? One AI Origin Story
- Video: Artificial Intelligence Terminology
- Video: Part I: How Does a Machine "Learn"?
- Video: Part II: How Does a Machine "Learn"?
- Video: What is Generative AI?
- Video: A Lens to Take With Us
- Graded Assignment: Module 1 Practice Quiz
- Reading: Module 1 Suggested Readings
- Reading: Module 1 Bibliography
Module 2: How Do Machine Learning Systems Work?
- Video: Introduction to How Do Machine Learning Systems Work
- Video: Ingredients of Machine Learning Part I: Data
- Video: Ingredients of Machine Learning Part II: Models
- Video: Ingredients of Machine Learning Part III: Evaluation
- Video: How Do We Know if a Machine Learning System is Useful?
- Video: More Usefulness Questions
- Graded Assignment: Module 2 Ungraded Quiz
- Reading: Module 2 Suggested Readings
- Reading: Module 2 Bibliography
Module 3: The Limits of Data and Prediction
- Video: Introduction to Putting AI Into Practice
- Video: The Why in AI
- Video: Case Study: Predicting Sepsis
- Video: Case Study: The Fragile Families Study
- Video: Does the Model Match the Problem?
- Graded Assignment: Module 3 Ungraded Quiz
- Reading: Module 3 Suggested Readings
- Reading: Module 3 Bibliography
Module 4: How to Have Better Conversations About AI
- Video: Introduction to How to Have Better Conversations About AI
- Video: Why is Talking About AI So Hard?
- Video: What Can Go Wrong Telling Stories About AI?
- Video: Avoid Thought-terminating Cliches
- Video: Course Recap: Decoding AI
- Discussion Prompt: Reflect and Respond to a News Story
- Graded Assignment: Module 4 Ungraded Quiz
- Reading: Module 4 Suggested Readings
- Reading: Module 4 Bibliography
- Reading: Post-Course Survey
- Graded: Comprehensive Course Exam
Grading Policy
This course is self-paced. You must earn an overall grade of 80% to pass and receive the certificate. There are four ungraded practice quizzes and a comprehensive test at the end of the course, worth 100% of your final grade.
Course content developed by U-M faculty and managed by the university. Faculty titles and affiliations are updated periodically.
Beginner Level
No previous knowledge on artificial intelligence is necessary
