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By definition, Machine Learning provides software with a way to learn something that it was not explicitly programmed for. In practice, Machine Learning is often used to allow people to learn something they might not have been able to do otherwise (e.g. infer relationships from large scale high-dimensional data at potentially high speed) and allow computers to learn something that humans learn naturally (e.g.: speaking, hearing, seeing). These two broad application areas of Machine Learning not only have a large impact on human behavior, their success often depends on a nuanced understanding of human behavior and how people interact with technology (i.e.: sociotechnical behavior). Students of the Human-Centered Machine Learning course will help form a new way of understanding and practicing the application of machine learning through a series of readings, discussions, and a final project.

Introduction

Prerequisite Education

Learning Objectives

Students who complete this course satisfactorally will...

Assignments and Grading

Below are the requirements and expectations of the class with the respective grade proportions.

HCML Project

Students will complete a final research paper that explores HCML in one of the following ways:

Students may also propose an alternative to one of these research paper formats.

The schedule and ruberic for the HCML project is as follows:

The HCML project will need to have a rough draft and a poster and/or demo completed by the 10th week of class for a poster/demo session that will be open to the public. The final draft will need to be completed at the beginning of finals week so that students can exchange and review each others' work.

Class Presentation and Leading Discussion

Students will lead discussion with a presentation twice in the quarter:

  1. One presentation will be leading a 45 minute discussion and presentation on research that is already included in the schedule. This presentation will also need to synthesize and incorporate reading responses from the other students.
  2. One presentation will be leading a 20 minute discussion and presentation on research, found by the student, that is related to the students' final project and enriches the conversation in human-centered machine learning. The purpose of this presentation is to facilitate an on-going conversation about how the practice and research of machine learning can be more "human-centered".

Reading Response

In order to better digest the readings, students will write a response to each weekly discussion prompt (provided in the reading schedule) by making appropriate citations and connections to the current week's readings and those of previous weeks. Students should feel free to cite other sources, but these should not be a replacement for the required readings of the week. This response will need to be uploaded by the Monday (subject to change) before class so that students will have a chance to read each others' responses and the presenter can integrate them into the weekly presentation. This exercise will provide students with the opportunity to get more comfortable with the writing and reflecting component of the class.

Class Attendance and Participation

In order to fully make the connections between the human-focused research and technology focused research in each week's readings it is important to participate in class discussion. Class discussion also helps students to practice communicating their understanding of the material to members of the class whose research focus and educational background skews toward human subjects and factors and those who skew toward computational research. Students will be required to attend each class and participate in discussion by asking thoughtful questions and communicating their opinions, ideas, and understanding on a topic clearly to everyone in the class.

Schedule

Week 1 : Introduction

Themes covered:

Discussion Prompt :

Readings :

Week 2 : Interactive Machine Learning 1 : Introduction

Themes Covered:

Discussion Prompt:

Readings:

Week 3 : Interactive Machine Learning 2 : Active Learning

Themes covered:

Discussion Prompt :

Readings :

Week 4 : Interactive Machine Learning 2: Reinforcement Learning

Themes covered:

Discussion Prompts:

Readings:

Week 5 : Model Personalization

Themes:

Discussion Prompt:

Readings:

Week 6 : Model Transparency

Themes:

Discussion Prompts:

Readings:

Week 7 : Algorithmic Bias, Accountability, and Auditing

Themes:

Discussion Prompts:

Readings:

Week 8 : Crowd ML 1

Themes:

Discussion Prompts:

Readings:

Week 9 : Crowd ML 2

Themes:

Discussion Prompts:

Readings:

Week 10 : Design Principles for Intelligent User Interfaces

Themes:

Discussion Prompts:

Readings:

Additional Resources:

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