Competitive Build Initiative
Team up with other MSAIL members for AI competitions and long-term hackathons.
Projects & Initiatives
The projects, courses, and reading groups MSAIL runs, plus the ML Discussion archive.
Fall 2026
Industry Project Team
Join a five-person team of student consultants and a project manager, working with industry professionals at the automotive supplier KPIT to build a full-scale automation tool from planning to deployment. Projects run at least a semester and focus on automating testing, safety, or company processes, with extensive full-stack development alongside agentic AI integration. Python experience is required. React, Next.js or Node.js, and database experience are preferred; industry experience is not required. We're looking for balanced teams with both frontend and backend experience.
Model Mining: Alignment & Interp
Hands-on work with language models: extracting the behaviors you want, then using evals and mechanistic interpretability to check alignment and understand what is going on inside. Tentative topics include SFT, DPO, RL, causal steering, evals, and patching. Neural network experience required; LLM or math background helps.
Building a Convolutional Neural Network
Learn how to build a convolutional neural network from the ground up over the course of the semester. This initiative is designed for everyone from beginners with no programming experience to students who already code but have never applied their skills to AI. We'll work through the fundamentals of Python, neural networks, image classification, model training, and evaluation, with each session building toward a final CNN of your own.
ML in the News
Come sit in as we break down AI and machine learning concepts through research paper summaries and deep dives of major news stories. Learn more about the technology you either can't get rid of or can't get enough of. Each week, we'll work to better understand the methods, claims, and real-world implications behind new developments in AI.
ML Discussion: Winter 2021 material
Material from the discussion group's Winter 2021 lesson series: slides, notebooks, and recordings, as the group posted them.
Classification with Logistic Regression
SlidesColab notebookLinear algebra primerPython basics (Kaggle)
Regression, Part 1 (Theory and Implementation)
Regression, Part 2 (Application)
Convolutional Neural Networks
Introduction and Basics of Deep Learning
Computer Vision
Natural Language Processing
Unsupervised Learning
Ethics
Lightning Round: Assorted AI Topics