36-708 The ABCDE of Statistical Methods in Machine Learning

12 units, Carnegie Mellon University

This course focuses on statistical methods for machine learning, a decades-old topic in statistics that now has a life of its own, intersecting with many other fields. While the core focus of this course is methodology (algorithms), the course will have some amount of formalization and rigor (theory/derivation/proof), and some amount of interacting with data (simulated and real). However, the primary way in which this course complements related courses in other departments is the joint ABCDE focus on (A) Algorithm design principles, (B) Bias-variance thinking, (C) Computational considerations (D) Data analysis (E) Explainability and interpretability.

Prerequisites: 36-705 (min A)

Offered: Spring 2027, Spring 2026

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