Assistant Professor of Electrical Engineering and Computer Science
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.
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
Lesson 1: From Real World to Task Environments
- Video: Mapping from the Real World to Task Environments
- Reading: Meet Your Instructor
- Reading: Syllabus
- Reading: Pre-Course Survey
Lesson 2: Task Environments
- Video: Types of Task Environments
- Reading: Task Environments to AI Techniques
- Reading: AI Technique Examples
Module 1 Wrap Up
- Practice Assignment: Map Out Task Environments
- Graded: Module 1 Quiz
Module 2: Making Predictions and Decisions
Lesson 1: Machine Learning
- Video: Mapping from Task Environments to Machine Learning
- Reading: Introductory Concepts for Machine Learning
- Reading: Linear Classification Introduction
- Video: Machine Learning
- Reading: Where to Learn More: Machine Learning
- Video: Special Challenges in Machine Learning for Social Impact
- Reading: Case Study 1: Deploying Machine Learning for Healthcare
Lesson 2: Reinforcement Learning
- Reading: Introductory Concepts for Reinforcement Learning
- Video: Reinforcement Learning
- Reading: Where to Learn More: Reinforcement Learning
- Video: Special Challenges in Reinforcement Learning for Social Impact
- Reading: Case Study 2: Human-AI Considerations for Public Health
Module 2 Wrap Up
- Practice Assignment: Identify the AI Technique
- Graded: Module 2 Quiz
- Reading: Post Course Survey
- Reading: Keep Learning with Michigan Online
Grading Policy
There are two quizzes in this course, each worth 50% of your final grade. Learners must earn an overall grade of 80% or higher in order to pass the course.
Course content developed by U-M faculty and managed by the university. Faculty titles and affiliations are updated periodically.
Beginner Level
Interest in real-world AI application, especially for social challenges. Some knowledge of machine learning concepts may be useful, but not required.
