Application form here

Course Description

The sustainable development goals (SDGs) encompass many important aspects of human and ecosystem well-being that are traditionally difficult to measure. This project-based course will focus on ways to use inexpensive, unconventional data streams to measure outcomes relevant to SDGs, including poverty, hunger, health, governance, and economic activity. Students will apply machine learning techniques to various projects outlined at the beginning of the quarter. The main learning goals are to gain experience conducting and communicating original research. Prior knowledge of machine learning techniques, such as from CS 221, CS 229, CS 231N, STATS 202, or STATS 216 is required. Open to both undergraduate and graduate students. Enrollment limited to 24. Students must apply for the class by filling out the form. A permission code will be given to admitted students to register for the class.

Note for co-term and masters students in CS: This class counts toward part (c) of the AI specialization.

Time and Location


Office Hours

TA Office Hours

Professor Office Hours

Units: 3-5

You may choose to take the class for 3, 4, or 5 units. There is no difference in workload or expectations.


Learning Goals

By the end of the quarter, students will be able to:

Class format

We encourage engagement during class - meaning listening closely to your peers and providing useful suggestions. To achieve this, we will have the following rules:

Class Schedule

The class will consist of three main parts: the first week will be background info, the next seven will focus on team development of their projects with regular presentations, and the last two weeks will focus on wrapping up and communicating results. Guidelines for each presentation will be given later.

Week Topics Items Due
  • Introduction to the SDGs
  • Review of syllabus
  • Overview of common datasets and tools you might want to use
  • Examples of prior projects
2 Group presentations. Summary of:
  • what others have done on this topic
  • what benchmarks are for performance on this or related tasks
  • what other sources of data might be useful
Literature review and slides for presentation
3 Group presentations.
  • Data visualization with basic summary plots/maps of your data E.g. what are typical images for high/low values of infrastructure
  • Discuss possible ideas for modeling
Slides for presentation
4 Group presentations. Show results from some baseline models using some simple reference model, e.g., regression Slides for presentation
5 Group presentations. Slides for presentation
6 Group presentations. Slides for presentation
7 Group presentations. Slides for presentation.
8 Group presentations. Slides for presentation.
9 Peer feedback session. Draft of final paper (Monday 3pm before class)
10 Final presentations. Slides for presentation. Final paper (Friday).

Grading Components

Weekly Group Presentations (week 2-8): 6 pts each
Peer-review report of another team’s paper: 8 pts
Final presentation and paper: 45 pts

Instructions and Rubrics:

Students will not be graded on whether they can successfully achieve their desired accuracies in predicting outcomes, given that most projects will be risky and not guaranteed to work. Students will be graded on devoting sufficient time to the project, clearly explaining progress and challenges, correctly applying techniques, and clearly writing up results. Successful projects will have the reward of paid trips to conferences (if the paper is accepted).

Length of weekly presentations will be determined by the number of projects. All students are expected to attend all sessions, and to give full attention and feedback to their classmates or instructors (no open laptops except for presenters).

Students will work in groups of 3 people. At the end of quarter, we will solicit feedback on your teammates and reserve the right to give individuals in the group higher or lower grades than the group average.

Project Topics

See here.


Marshall Burke

Marshall Burke

Earth System Science
Email: mburke [at]

Stefano Ermon

Stefano Ermon

Computer Science
Email: ermon [at]

David Lobell

David Lobell

Earth System Science
Email: dlobell [at]


Course Assistants


Robin Cheong

Computer Science
Email: robinc20 [at]


Matthew Tan

Computer Science
Email: mratan [at]