Schedule

Readings in normal font should be completed and annotated ahead of lecture.
Readings in italic provide optional additional depth on the material.
Assignments are listed on the day when I suggest you begin working on them.

Reading sources:

PSC: Lecture notes I’ve written for this course, hosted here.
PDSH: The Python Data Science Handbook by Vanderplas (2016).
BHN: Fairness and Machine Learning: Limitations and Opportunities by Barocas, Hardt, and Narayanan (2023).

Week 1

Mon Feb. 10	Welcome!
Mon Feb. 10	We introduce our topic and discuss how the course works.
	Learning Objectives Getting Oriented	Reading Course syllabus	Notes Welcome slides Data, Patterns, and Models	Warmup Set up your software.	Assignments Math pre-assessment.
Wed Feb. 12	The Classification Workflow in Python
Wed Feb. 12	We work through a simple, complete example of training and evaluating a classification model on a small data set.
	Learning Objectives Navigation Experimentation	Reading PDSH: Data Manipulation with Pandas (through "Aggregation and Grouping")	Notes Lecture notes Live notes	Warmup Manual linear prediction	Assignments Blog Post: Penguins

Week 2

Mon Feb. 17	Linear Score-Based Classification
Mon Feb. 17	We study a fundamental method for binary classification in which data points are assigned scores. Scores above a certain threshold are assigned to one class; scores below are assigned to another.
	Learning Objectives Theory Experimentation	Reading Linear Classifiers from MITx.	Notes Lecture notes Live notes	Warmup Decision Boundaries
Wed Feb. 19	Statistical Decision Theory and Automated Decision-Making
Wed Feb. 19	We discuss the theory of making automated decisions based on a score function. We go into detail on thresholding, error rates, and cost-based optimization.
	Learning Objectives Theory Experimentation	Reading PDSH: Introduction to Numpy	Notes Lecture notes Live notes	Warmup Choosing a Threshold	Assignments Blog Post: Design and Impact of Automated Decision Systems

Week 3

Mon Feb. 24	Auditing Fairness
Mon Feb. 24	We introduce the topics of fairness and disparity in automated decision systems using a famous case study.
	Learning Objectives Social Responsibility Experimentation	Reading BHN: Introduction Machine Bias by Julia Angwin et al. for ProPublica.	Notes Lecture notes Live notes	Warmup Experiencing (Un)Fairness
Wed Feb. 26	Statistical Definitions of Fairness in Automated Decision-Making
Wed Feb. 26	We offer formal mathematical definitions of several natural intuitions of fairness, review how to assess them empirically on data in Python, and prove that two major definitions are incompatible with each other.
	Learning Objectives Social Responsibility Theory	Reading BHN: Classification (ok to skip "Relationships between criteria" and below)	Notes Lecture notes Live notes	Warmup BHN Reading Check	Assignments Blog Post: Auditing Bias OR Blog Post: Bias Replication Study

Week 4

Mon Mar. 03	Normative Theory of Fairness
Mon Mar. 03	We discuss some of the broad philosophical and political positions that underly the theory of fairness, and connect these positions to statistical definitions.
	Learning Objectives Social Responsibility	Reading BHN: Relative Notions of Fairness	Notes Discussion guide shared on Canvas	Warmup COMPAS and Equality of Opportunity
Wed Mar. 05	Critical Perspectives
Wed Mar. 05	We discuss several critical views that seek to move our attention beyond the fairness of algorithms and towards their role in sociotechnical systems. We center two questions: who benefits from a given data science task? What tasks could we approach instead if our aims were to uplift the oppressed?
	Learning Objectives Social Responsibility	Reading Data Feminism: The Power Chapter by Catherine D'Ignazio and Lauren Klein "The Digital Poorhouse" by Virginia Eubanks "Studying Up: Reorienting the study of algorithmic fairness around issues of power" by Barabas et al.	Notes Discussion guide shared on Canvas	Warmup Power, Data, and Studying Up	Assignments Blog Post: Limitations of the Quantitative Approach

Week 5

Mon Mar. 10	No class
Mon Mar. 10	Phil is giving a talk at Michigan State

Wed Mar. 12	Introduction to Model Training: The Perceptron
Wed Mar. 12	We study the perceptron as an example of a linear model with a training algorithm. Our understanding of this algorithm and its shortcomings will form the foundation of our future explorations in empirical risk minimization.
	Learning Objectives Theory	Reading No reading today, but please be ready to put some extra time into the warmup. It may be useful to review our lecture notes on score-based classification and decision theory when completing the warmup.	Notes Lecture notes Live notes	Warmup Linear Models, Perceptron, and Torch	Assignments Blog Post: Implementing Perceptron

Week 6

Mon Mar. 17	Spring Break!
Mon Mar. 17

Wed Mar. 19	Spring Break!
Wed Mar. 19

Week 7

Mon Mar. 24	Convex Empirical Risk Minimization
Mon Mar. 24	We introduce the framework of convex empirical risk minimization, which offers a principled approach to overcoming the many limitations of the perceptron algorithm.
	Learning Objectives Theory	Reading Convexity Examples by Stephen D. Boyles, pages 1 - 7 (ok to stop when we start talking about gradients and Hessians).	Notes Lecture notes Live notes	Warmup Practice with Convex Functions

Week 8

Mon Mar. 24	Gradient Descent
Mon Mar. 24	We study a method for finding the minima of convex functions using techniques from calculus and linear algebra.
	Learning Objectives Theory	Reading No reading today, but please budget some extra time for the warmup.	Notes Lecture notes Live notes	Warmup A First Look at Gradient Descent	Assignments TBD
Wed Mar. 26	Feature Maps and Regularization
Wed Mar. 26	We re-introduce feature maps as a method for learning nonlinear decision boundaries, and add regularization to the empirical risk minimization problem in order to control the complexity of our learned models.
	Learning Objectives Theory Experimentation	Reading No reading today -- please think hard about your project pitches!	Notes TBD TBD	Warmup Project Pitches

References

Barocas, Solon, Moritz Hardt, and Arvind Narayanan. 2023. Fairness and Machine Learning: Limitations and Opportunities. Cambridge, Massachusetts: The MIT Press.

Vanderplas, Jacob T. 2016. Python Data Science Handbook: Essential Tools for Working with Data. First edition. Sebastopol, CA: O’Reilly Media, Inc.