Decoding AI: A Deep Dive into AI Models and Predictions
This course is part of Understanding Data: Stats, Science, and AI Explained.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Understanding Data: Navigating Statistics, Science, and AI Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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What you'll learn
Understand fundamental AI and machine learning concepts
Evaluate AI system capabilities and limitations
Analyze data requirements for machine learning
Identify potential biases and ethical concerns in AI
Assess real-world AI applications critically
Navigate conversations about AI technology effectively
Skills you'll gain
This course includes:
3.4 Hours PreRecorded video
5 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This foundational course demystifies artificial intelligence and its applications. Students learn core concepts in machine learning, including data requirements, model development, and evaluation techniques. The curriculum covers both technical aspects and practical implications of AI systems, with special focus on generative AI and deep learning. Through case studies and real-world examples, learners develop critical thinking skills to evaluate AI capabilities and limitations, understand potential biases, and identify common misconceptions about AI technology.
Welcome, Introduction and What Does "Artificial Intelligence" Really Mean?
Module 1 · 2 Hours to complete
How Do Machine Learning Systems Work?
Module 2 · 2 Hours to complete
The Limits of Data and Prediction
Module 3 · 2 Hours to complete
How to Have Better Conversations About AI
Module 4 · 3 Hours to complete
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Understanding Data: Stats, Science, and AI Explained
Instructor
Lecturer IV & Research Investigator
Elle O'Brien is a lecturer and research investigator at the University of Michigan. She completed her MS in neuroscience and PhD in hearing science at the University of Washington. Elle spent a year working at an open-source software startup training data professionals to adopt principles from software engineering to make their analyses more reproducible. Now as a researcher and lecturer at the University of Michigan School of Information, Elle designs and teaches graduate courses about statistics and data science. She is also running a research program to study how scientists adopt new software, analysis methods, and technology.
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Frequently asked questions
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