Applications

Applications for Winter Quarter 2018 are closed. We will not be offering this class in Spring Quarter.

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.

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

Time and Location

Meetings

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.

Syllabus

Learning Goals

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

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
1
  • Introduction to the SDGs
  • Overview of project choices
  • Review of syllabus
  • Overview of common datasets and tools you might want to use
  • Examples of prior projects
None
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
  • Discuss possible ideas for modeling
Slides for presentation
4 Group presentations. Show results from some baseline models such as regression.
  • Simple models. 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. Draft of final paper.
9 Peer feedback session. Written review of another team's paper.
10 Final presentations. Slides for presentation. Final paper.

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:

  1. No open laptops during class
  2. All slides must be submitted on a common google slide deck by 1pm on the day of class. The TAs will send out a link to the slides for each week.
  3. Talks are limited to 12 minutes, with 5 minutes for questions/discussion after.
  4. Groups will be randomly assigned each week to the “1st half” or “2nd half” sessions. Three groups will present in 1st half, then we will have a 15 minute break, then three groups will present in 2nd half. You only need to attend the half that your group is in. You can use the rest of class time to work on your project outside of class.
  5. TAs will also circulate guidelines for presentations, such as not to show raw code, not to show more than 2 significant digits, not to spend a lot of time on parts you don’t want feedback on, etc.
  6. After each class, TAs will send out a quick survey where you vote for the best presentation among the others in your session, and give a reason why you think it was best.

Grading Components

Weekly Group Presentations (week 2-8): 6 pts each
Participation in weekly surveys: 5 pts Individual peer-review report of another team’s paper: 8 pts
Final presentation, paper (to be written in the form of a short conference paper), and code repository: 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. We expect that each member of the team contribute in both technical and non-technical components. 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.

Professors

Marshall Burke

Marshall Burke

Earth System Science
Email: mburke [at] stanford.edu

Website
Stefano Ermon

Stefano Ermon

Computer Science
Email: ermon [at] cs.stanford.edu

Website
David Lobell

David Lobell

Earth System Science
Email: dlobell [at] stanford.edu

Website

Course Assistants

Noa Glaser

Noa Glaser

Computer Science
Email: noaglasr [at] stanford.edu

Website
Jihyeon Lee

Jihyeon Janel Lee

Computer Science
Email: jlee24 [at] stanford.edu

Website