Assistant Professor of Electrical Engineering and Computer Science, College of Engineering
Practical AI for Social Impact
What You'll Learn
- Explain the foundational concepts of real-world modeling that influence which AI techniques we can use in AI for social impact scenarios
- Describe several AI algorithmic techniques frequently used in AI for social impact, and when they are used
- Identify practical considerations when deploying these algorithms
2 Modules
4 Hours
2 hrs per module (approx.)
About Practical AI for Social Impact
Gain a foundational understanding of key AI algorithms and build valuable skills highly sought after in tech and social impact fields. "Practical AI for Social Impact" introduces you to algorithms, including machine learning and reinforcement learning, with additional topics and resources referenced throughout the course.
Crucially, this course also addresses special challenges in implementing AI for social impact. Throughout the course, we’ll discuss how to navigate data scarcity, bias, and ethical deployment in real-world communities. Case studies will explore potential scenarios with sequential, adaptive decisions, helping you approach new situations with tact and knowledge.
By the end of the course, you’ll have developed skills highly sought after in tech and nonprofit sectors, empowering you to optimize resource distribution and drive systemic change at scale.
This is the second course in the three-course series, "Realizing AI for Social Impact", where you will explore use cases and frameworks for deploying AI to achieve social impact.
Skills You'll Gain
- Artificial Intelligence
- Community Advocacy
- Data Literacy
- Data Management
- Data Quality
- Social Justice
What You'll Earn
- Certificate of Completion
- Certificates of completion acknowledge knowledge acquired upon completion of a non-credit course or program.
Course content developed by U-M faculty and managed by the university. Faculty titles and affiliations are updated periodically.
Welcome Message
Welcome to Practical AI for Social Impact! This is the second course in a three-part series, Realizing AI for Social Impact, from the University of Michigan. In this course, we discuss modeling real-world challenges for potential AI solutions, as well as high-level machine learning and reinforcement learning solution techniques. We hope this course inspires you to start trying to apply AI techniques in your context!
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: Modeling the Real World
- Explores how real-world problems can be translated into task environments that AI systems can work with.
- Examines different types of task environments and how they connect to AI techniques.
- Provides opportunities to map examples and build a foundation for applying AI in practice.
Module 2: Making Predictions and Decisions
- Examines how AI systems learn and act through machine learning and reinforcement learning.
- Introduces core concepts, practical challenges, and real-world case studies in healthcare and public health.
- Develops skills for identifying which AI techniques best fit social impact contexts.
Grading Policy
Course materials and assignments remain open and available for self-paced learning. To pass the course and earn the certificate, you must achieve an overall grade of 80% or higher.
Your final grade is based entirely on the two module quizzes. Each quiz assesses your understanding of the material covered in its respective module and contributes equally to your overall course grade.
Quizzes — 100% of the Course Grade
- Module 1 Quiz: 50%
- Module 2 Quiz: 50%
Total Course Grade: 100%
Beginner Level
Interest in real-world AI application, especially for social challenges. Some knowledge of machine learning concepts may be useful, but not required.
