Information

Designing Machine Learning is a project by the Stanford d.School to make Machine Learning (ML) more accessible to innovators from all disciplines. We believe that ML will soon be a widespread feature of products, services, systems, and experiences in all walks of life. In order to make this computational revolution possible, as well as to ensure that it closely follows human values, we must empower a new generation of professionals to incorporate ML into their creative process.

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Motivation

Design is the exploration of possibility, at once a critique of the present and a supreme optimism about human ingenuity. In a way, machines are design made manifest. Machines are programmable constructs, giving limitless potential and new forms of agency to the designer. Machine learning is yet another axis of empowerment, still incipient in its capabilities. Perhaps it is the humility with which we have previously approached machine learning that has prevented us from applying it pervasively, or exposing it to wider inquiry.

Traditionally, designers have differed from data scientists in their approach to building systems and their metrics of measuring success. At its core, this is a difference of values that pervades each field. Data science is statistical and numerical, with focus placed on standardized measures, such as “training versus test accuracy”. To designers, this (and many other) metrics are essentially arbitrary. What matters to designers is the human value ascribed to the experience of the product. How do we reconcile this stark contrast?

Purpose-Built vs Outcome-Oriented The discontinuity in values between fields

Course Information

Office Hours

As a courtesy, for appointments please schedule at least four hours in advance to give us time to prepare.

  • Abhay’s Office Hours: Abhay’s Scheduling Link
  • Michelle’s Office Hours: By appointment via e-mail (michelle.r.carney@gmail.com) and Wednesdays from 2PM-3PM in d.School Bay Studios (please e-mail to confirm space).
  • Anand’s Office Hours: By appointment via e-mail (datawocky@gmail.com) and Thursdays from 5PM-6PM in d.School Bay Studios (please e-mail to confirm space).

Music From Class

Week 1

Topics covered: Intro to Machine Learning, Decision-Making Systems, Class Logistics.

Assignments Due Next Week

Readings Due Next Week

Week 2

Topics covered: Understanding the Data Science Pipeline. Data Collection Systems. Content Filtering in Wikipedia

Content From Class

Assignments Due Next Week

  • Presentations for first guided investigation, Wikipedia Abuse Filter
  • Fill out and bring your brainstorming sheets along with print-outs of any materials you will turn in.

Readings Due Next Week

Week 3

Topics covered: Machine Learning + User Experience. Supervised Learning. Recommender Systems & Netflix Prize.

Content From Class

Assignments Due Next Week

  • Presentations for second guided investigation, Netflix Recommendation System
  • Fill out and bring your brainstorming sheets along with print-outs of any materials you will turn in.

Readings Due Next Week

Week 4

Topics covered: Present Netflix Projects. Prototyping in ML. Data Visualization & Data Exploration. Clustering Data. Project on Meetup Data Set.

Content From Class

Assignments Due Next Week

Readings For Next Week

Resources Mentioned In Class

Week 5

Topics covered: Introduction to Neural Network Systems and High-Dimensional Classifiers. Image & Video Processing.

Content From Class

Assignments Due Next Week

Readings For Next Week

Week 6

Topics covered: Present Meetup Project Work. Continued Study on Neural Networks. Generative Design & GANs.

Content From Class

Assignments Due Next Week

  • None

Readings For Next Week