Week 1

Lecture

• Lecture #1: Course Overview
  • Lecture Video
  • Lecture Notes
  • Lecture Summary

Activities

• In-Class Exercise #1: Introduction to Linear Regression
• In-Class Exercise #1: Solutions
• Homework #0
• Homework #0 Solutions

Reading

Preparatory Readings:

• How to Assess the Broader Impact of Technology
• How To Read Machine Learning Papers: A Guide

Promises of Machine Learning:

1. (Introductory) From Diagnosis to Holistic Patient Care, Machine Learning is Transforming Healthcare
2. (Introductory) The future of agriculture is computerized
3. (In-Depth) Tackling Climate Change with Machine Learning
4. (In-Depth) Machine Learning for the Developing World
5. (In-Depth) AI for social good: unlocking the opportunity for positive impact

Potential Dangers of Machine Learning:

1. (Introductory) Predictive policing algorithms are racist. They need to be dismantled.
2. (Introductory) When Good Algorithms Go Sexist: Why and How to Advance AI Gender Equity
3. (Introductory) Millions of black people affected by racial bias in health-care algorithms
4. (Introductory) Medicine’s Machine Learning Problem
5. (Introductory) Algorithms Are Making Economic Inequality Worse
6. (Introductory) AI needs to face up to its invisible-worker problem
7. (In-Depth) A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions

Machine Learning and Ethics:

1. (Introductory) Artificial Intelligence in Health: Ethical Considerations for Research and Practice
2. (In-Depth) Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward
3. (In-Depth) Data Science as Political Action: Grounding Data Science in a Politics of Justice

Linear Regression:

1. (Introductory) Linear regression
2. (Introductory) Linear Regression via Maximization of the Likelihood

Bayesian Linear Regression:

1. (In-Depth) Bayesian Linear Regression
2. (In-Depth) Bayesian Linear Model: Gory Details