Patterns, Predictions, and Actions (2025)

I’ll be live blogging my graduate course on machine learning this semester (Fall 2025). The course is based on the text Patterns, Predictions, and Actions by Moritz Hardt and myself.

This course will explore how patterns in data support predictions and consequential actions. Starting with the foundations of prediction, we look at the foundational optimization theory used to automate decision-making. We then turn to supervised learning, covering representation, optimization, and generalization as its key constituents. We will discuss datasets as benchmarks, examining their histories and scientific bases. We will then cover the related principles of statistical evaluation, drawing a through line from confidence intervals to AB testing to bandits to reinforcement learning. Throughout the course, we will draw upon connections to historical context, contemporary practice, and societal impact.

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1. Lecture 1: Introduction

   1. Slides (I'm not sure if these are helpful without my narration.)

2. Lecture 2: Rudiments of Prediction

   1. Learning from clairvoyance

   2. Lecture Notes

3. Lecture 3: Prediction from Samples (without features)

   1. Your noise is my signal

   2. The Actuary's Final Word

   3. Lecture Notes

   4. Recht, Benjamin (2025) “The Actuary’s Final Word on Algorithmic Decision-Making.” arXiv:2509.04546. [slides]

4. Lecture 4: Decision Theory

   1. Justify your answer

   2. Reading: Chapter 2 of PPA.

5. Lecture 5: Errors, Operating Characteristics, and Tradeoffs.

   1. Stuck in the middle

   2. Reading: Chapter 2 of PPA.

6. Lecture 6: Fairness and trade-offs (by Jessica Dai)

   1. Reading: PPA Chapter 2.

   2. Hardt et al. Equality of Opportunity in Supervised Learning.

   3. Kleinberg et al. Inherent Trade-Offs in the Fair Determination of Risk Scores.

7. Lecture 7: The Perceptron

   1. Common Descent

   2. Reading: PPA Chapter 3.

8. Lecture 8: Numerically representing data

   1. Boxes of numbers

   2. Reading: PPA Chapter 4.

9. Lecture 9: Nonlinearity and approximation

   1. Universal Cascades

   2. Reading: PPA Chapter 4.

10. Lecture. 10: Stochastic Gradient Descent

    1. Highly optimized optimizers

    2. Reading: PPA Chapter 5.

11. Lecture 11: Analysis of the Stochastic Gradient Method

    1. Minimal Theory

    2. Reading: PPA Chapter 5.