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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.
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.
Resources
Learning Materials (Links)
General Information
- Language
- English
- Levels
- BSC , DR , MSC
- Frequency
- Yearly recurring
Examination
- Type
- graded semester performance
Registration & Places
- Max Places
- 15
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise |
#AI4Impact: Machine Learning for Social Impact
Does not take place this semester.
|
No time listed | 2 h weekly |
Offered In
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Electives (This is only a short selection. Other courses from the ETH course catalogue may be chosen. Please consult the "Richtlinien zu Projekten, Praktika, Seminare" (German only), .)
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Electives (The entire course programs of ETH Zurich and the University of Zurich are open to the students to individual selection.)
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Electives (The entire course programs of ETH Zurich and the University of Zurich are open to the students to individual selection. The students have themselves to check whether they meet the admission requirements for a course.)
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Doctorate Civil, Environmental and Geomatic Engineering (More Information at: )
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Subject Specialisation (In addition to the courses listed below, D-BAUG doctoral students are free to choose from the entire range of subject-specific courses offered by ETHZ and the University of Zurich, provided that it is an offering specifically designed for doctoral students or a course of the regular Master’s program or of the third year Bachelor’s program.)
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