Advanced Topics in Machine Learning
Large Language Models for Reasoning
(CS 159, Spring 2025)
Large Language Models for Reasoning
(CS 159, Spring 2025)
Large Language Models (LLMs) are an exciting new development in machine learning and artificial intelligence. This course explores research directions in using LLMs as a strong base model to enable reasoning tasks.
The goal in this course is for students to be able to:
Learn about range of reasoning tasks. (Lectures)
Gain operational understanding of how to use LLMs within applications. (Colab Notebooks)
Learn to deconstruct research papers. (Paper presentation)
Formulate challenges in applying LLMs to new reasoning tasks. (Project Proposal)
Explore aforementioned challenges as a research project. (Final Project)
Those interested in learning more about the architecture of LLMs and other Generative AI models should take EE 148.
Prerequisites:
CS 155 (Machine Learning & Data Mining) -- Hard Prerequisite
EE 148 (Large Language and Vision Models) -- Soft Prerequisite
Instructors and Teaching Assistants
Yisong Yue (yyue@caltech.edu) -- Head Instructor
Hao Liu (hliu3@caltech.edu) -- Teaching Assistant
Alex Farhang (afarhang@caltech.edu) -- Teaching Assistant
Geeling Chau (gchau@caltech.edu) -- Teaching Assistant
Daniel Khalil (dkhalil@caltech.edu) -- Teaching Assistant
Weeks 1 & 2: Introductory Lectures
Weeks 3-10: Student Presentations & Guest Lectures
Week 5: Project Proposal
Week 10: Project Presentation and/or Poster Session
We will use Piazza for course announcements.
90% of the grade is on the final project. 10% of the grade is on the presentation.
Student presentations will revolve around a specific paper. Each paper will have four presenters taking on the roles of: Champion, Critic, Pioneer, Entrepreneur.
The final project should be done in groups (recommended group size is 2-3, max is 4), and one final project document should be submitted per group. The deliverables are: a project proposal due on TBD, and the final project report due at the last day of class (exact date TBD).
Tentative: Students will also have an opportunity to present their project in a poster session at the end of the term.