Instructor: Justin Gottschlich (goju@stanford.edu)
When: Tuesdays 6:00-8:50pm
Building: History Corner (Building 200), Room 200-002
Course Assistant: Anni Zimina (zimina@stanford.edu)
Course Credits: CS 329M (3-4 credits)
Dates: September 22 - December 1 (2026)
Stanford CS 329M Lectures (September - December 2026):
[Week 01] Sept. 22 / Lecture 1 - Fundamentals of Machine Programming
[Week 02] Sept. 29 / Lecture 2 - Tour of the State of MP (e.g., Claude Code, GitHub Copilot, Cursor, Devin Desktop (Windsurf), Gemini Code Assist, Lovable, Replit Agent, etc.)
[Week 03] Oct. 6 / Lecture 3 - Core Tenets & First Principles of ML / MP, Deeper Dive into 4-3-2 of MP
[Week 04] Oct.13 / Lecture 4 - Principles of Data: Ground Truth, Semi-Trust, and Static / Continuous Learning
[Week 05] Oct. 20 / Lecture 5 - Meta-Data, Meta-Reasoning, Latent Spaces & Higher-Dimensional Analyses
[Week 06] Oct. 27 / Lecture 6 - Performance Implications of MP - Cloud, Local, Hybrid, and Other
[Week 07] Nov. 3 / Stanford Democracy Day (Classes Canceled)
[Week 08] Nov. 10 / Lecture 7 - Classical & Future Software Engineering (SE) with Temporal Reasoning
[Week 09] Nov. 17 / Lectures 8 - Future of Programming & Programming Languages (HIPLs, MIPLs, Intentional PLs, etc.)
[Week 10] Nov. 24: Autumn Break (No Classes)
[Week 11] Dec. 1 / Lecture 9 - Future of Machine Programming & the Criticality of the Next Decade
Course Description
The field of machine programming (MP) is concerned with the automation of software development. Given the recent advances in software algorithms, hardware efficiency and capacity, and an ever increasing availability of code data, it is now possible to train machines to help develop software. In this course, we teach students how to build real-world MP systems. We begin with a high-level overview of the field, including an abbreviated analysis of state-of-the-art (e.g., code assistants). Next, we discuss the foundations of MP and the key areas for innovation, some of which are unique to MP. We close with a discussion of current limitations and future directions of MP. This course includes a nine-week hands-on project, where students (as individuals or in a small group) will create their own MP system and demonstrate it to the class.
While some overlap exists between traditional techniques to train machines to perform non-programming tasks (e.g., natural language processing, computer vision, etc.), teaching machines to perform programming-specific tasks has uniqueness in at least two dimensions. First, there are certain techniques that are more (or less) effective for MP, such as using self-supervision to learn from the large corpora of unlabeled open-source code. Second, software reasoning is fundamentally multi-dimensional; that is, there exist multiple unique ways to learn from software (e.g., static analysis, dynamic analysis, input/output specifications, program state reinforced-convergence, hardware telemetric data, etc.). In this course, we discuss each of these techniques (and others) and how they can be effectively applied to MP systems.
This course is primarily intended for Stanford MS and PhD graduate students. However, advanced (senior-level) and committed undergraduates have successfully completed this course. Hard-working and disciplined students have historically done well in CS 329M even if they are relatively new to ML, PL, SE, and systems.
Examples of Notable Prior CS 329M Projects:
The following short list of papers are exemplars of CS 329M student projects. Each target a different subdomain within the field of MP. Even though they are diverse from each other, they represent a small fraction (< 1%) of all possible domains you can target for your CS 329M project.
"Un-reinventing the Wheel: Large Scale Open Source Code Search" (slides, Hellman and Spector)
"Evaluating and Improving the Security of AI-Based Code Assistants" (slides, Gu)
"Side Effect Detection through Meta-reasoning and Learning" (Li and Zhu)
2026 Reading List
Week 1
Three Pillars of Machine Programming (Gottschlich et al., ACM MAPL 2018)
Challenges and Paths Toward AI for Software Engineering (Gu et al., ICLR VERIF-AI workshop 2025)
Week 2
The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers (Lee et al., CHI 2025)
SWE-bench: Can Language Models Resolve Real-World GitHub Issues? (Jimenez et al., ICLR 2024)
AI Programmer: Autonomously Creating Software Programs Using Genetic Algorithms (Becker and Gottschlich, GECCO 2021)
<More reading material added as we move forward in the quarter>
CS 329M Material from Prior Years
Stanford CS 329M Lectures (September - December 2024)
[Week 01] Sept. 23, 25: Lectures 1 & 2
[Week 02] Sept. 30, Oct. 2: Lectures 3 & 4
[Week 03] Oct. 7, 9: Lectures 5 & 6
[Week 04] Oct. 14, 16: Lectures 7 & 8
[Week 05] Oct. 21, 23: Lecture Canceled, Student Checkpoint Presentations
[Week 06] Oct. 28, 30: Lectures 9 & 10
[Week 07] Nov. 4, 6: Lectures 11 & 12
[Week 08] Nov. 11, 13: Lectures 13 & 14
[Week 09] Nov. 18, 20: Lectures 15 & 16
[Week 10] Nov. 25, 27: Autumn Break (No classes)
[Week 11] Dec. 2, 4: Invited Guest Lectures Day, Student Presentations
2023 Lectures:
Stanford CS 329M Lectures (September - December 2023):
[Week 05] October 22-28: Lecture 9 , <Student Presentations>
[Week 06] October 29-Nov 4: Lecture 10, Lecture 11
[Week 07] November 5-11: <Stanford Democracy Day; no class>, Lecture 12
[Week 08] November 12-18: Lecture 13, Lecture 14
[Week 09] November 19-25: <Autumn break; no class>
[Week 10] November 26-December 2: Lecture 15, Lecture 16
[Week 11] December 3-9: Lecture 17, <Student Presentations>
[Week 12] December 10-16: Finals Week (Take-home Final Exam, due midnight on Dec. 14)
2022 Lectures:
Part 1: Lecture 1. Lecture 2. Lecture 3. Lecture 4. Lecture 5. Lecture 6. Lecture 7. Lecture 8. Lecture 9.
Part 2: Lecture 10. Lecture 11. Lecture 12. Lecture 13. Lecture 14 (guest lecture). Lecture 15.
Part 3: Lecture 16 (student presentations). Lecture 17 (student presentations).