Schedule for CMU 10-718: Spring 2026

Week	Date	Class Type	Topic	Resources	Announcements
1	Tue Jan 13	lecture	Intro and Overview - History, Motivation, Logistics [Slides]	Optional readings H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models, Zhang et al 2023 Efficient streaming LMs with attention sinks, Xiao et al 2024	Feedback to give Give feedback Signup to present Signup
1	Thu Jan 15	lecture	ML Project Deep Dive - Project definition and scoping [Slides]	Optional readings project scoping guide project scoping worksheet	Required readings Artificial Intelligence—The Revolution Hasn’t Happened Yet The bitter lesson
2	Tue Jan 20	lecture	Data Preparation - Scraping, Data Integration, Visualization [Slides]		Deadlines Signup to present in first 1/2 of course Required readings Debugging Large-Scale Data Science Pipelines using Dagger, Rezig et al, VLDB 2020 Goal Driven Discovery of Distributional Differences via Language Descriptions
2	Thu Jan 22	lecture	Problem Formulation - How ML maps to real world problems [Slides]		Required readings Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations Here’s What You Should Know About Launching an AI Startup - WIRED 2025 Here’s What You Should Know ... WIRED (cached version)
3	Tue Jan 27	lecture	Data & Feature Engineering [Slides]		Deadlines Project Assignment 0 due (team and alpha version of proposal) Required readings Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks Can Foundation Models Wrangle Your Data?
3	Thu Jan 29	lecture	Evaluation [Slides]		Required readings Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling, ACL 2024 Amazing Things Come From Having Many Good Models, ICML 2024 Prediction Powered Inference
4	Tue Feb 3	lecture	Project Checkins 1		Deadlines Project Assignment 1 due (project checkins)
4	Thu Feb 5	lecture	Project Checkins 2
5	Tue Feb 10	lecture	Hyperparameter tuning [Slides]		Required readings Google Vizier: A Service for Black-Box Optimization (KDD 2017)
5	Thu Feb 12	lecture	Interpretability		Required readings The Mythos of Interpretability, Lipton 2018
6	Tue Feb 17	lecture	Project Pitches		Deadlines Project Assignment 2 due (project pitches)
6	Thu Feb 19	lecture	Project Pitches
7	Tue Feb 24	lecture	Privacy
7	Thu Feb 26	lecture	Distribution Shift and Robustness
8	Tue Mar 3	lecture	Spring break 1 - no class
8	Thu Mar 5	lecture	Spring break 2 - no class
9	Tue Mar 10	lecture	Fairness		Deadlines Project Assignment 3 due (initial result writeups)
9	Thu Mar 12	lecture	Safety
10	Tue Mar 17	lecture	Model Compression
10	Thu Mar 19	lecture	Uncertainty and Calibration
11	Tue Mar 24	lecture	Causality
11	Thu Mar 26	lecture	ML and Intervention
12	Tue Mar 31	lecture	Project Presentations - 1		Deadlines Project Assignment 4 due (proposed extensons)
12	Thu Apr 2	lecture	Project Presentations - 2
13	Tue Apr 7	lecture	Network Effects and ML
13	Thu Apr 9	lecture	Carnival - no class
14	Tue Apr 14	lecture	TBA
14	Thu Apr 16	lecture	TBA
15	Tue Apr 21	lecture	Final Project Presentations - 1		Deadlines Project Assignment 5 due (final writeup and reflections)
15	Thu Apr 23	lecture	Final Project Presentations - 2