363-1191-00L #AI4Impact: Machine Learning for Social Impact
| Semester | Spring Semester 2025 |
| Lecturers | |
| Periodicity | yearly recurring course |
| Course | Does not take place this semester. |
| Language of instruction | English |
| Abstract | How can AI be leveraged to make real-world impact? This course will introduce students to the fundamentals of machine learning (ML) in a hands-on manner with a focus on applying them to address challenges that will impact people's lives in areas such as health, education, legal, and the UN Sustainable Development Goals more broadly. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | This course seeks to introduce students without prior machine learning (ML) experience backgrounds to the fundamentals of ML and give them hands-on skills to apply ML to solve problems that make real-world impact. Students will learn machine learning concepts such as classification, regression, deep learning, natural language processing, and generative AI, and apply them to real-world datasets in hands-on labs. Furthermore, students will learn to work together in teams to develop ML systems that make real-world impact. After taking this course, students will be able to explore and preprocess data, engineer and select relevant features, train relevant ML models, and conduct thorough experiments to evaluate model performance using appropriate metrics. The hope is that learners will leave the course adequately equipped and inspired to use their newly acquired ML superpowers to make the world a much better place! | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | This course seeks to introduce students without prior machine learning (ML) experience backgrounds to the fundamentals of ML and give them hands-on skills to apply ML to solve problems that make real-world impact. Programming experience in Python is a requirement. No prior experience with machine learning is required. The course is structured in lectures, hands-on coding exercises, assignments, and course projects. Lectures In the lectures, the students will be introduced to the fundamentals of ML along with relevant applications. Various topics include algorithms for classification, regression, deep learning, natural language processing, generative AI, and ML pipelines consisting of data exploration and preprocessing, feature extraction and engineering, model training, and evaluation. Lectures will include in-class coding exercises and discussions. It will also feature guest lecturers (e.g., practitioners) who will give talks on ML systems that they have developed and deployed for impact. Assignments Students will work individually to apply the ML concepts introduced in the lectures on provided datasets for impact. Emphasis will be placed on facilitating an intuitive and hands-on understanding of ML models and how to make them work on messy real-world datasets and contexts. Course Projects The course project will put everything together and will be the key deliverable. Students will work collaboratively in teams to implement an ML system for social impact, write a paper on the work with the caliber to be accepted at an applied ML conference in the relevant domain, and present it. Students will be responsible for finding relevant datasets for use. We will explore collaborations with NGOs and companies to make available relevant datasets to use for the project. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | (1) Programming experience in Python (2) Passionate about making social impact with technology | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Competencies |
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