Overview

This course provides a broad perspective on AI, with a focus on foundational principles powering modern AI. This course will first build towards what is now colloquially refered to as AI in current days: large language models and generative AI. We will then study classical AI topics of search, reinforcement learning, and game theory to provide a well-rounded understanding of how AI systems can reason, learn, and make decisions. The course will explore the connections between these classical techniques and modern AI approaches, highlighting how foundational ideas have evolved and influenced current advancements. Through a combination of lectures offering a mathematical perspective, hands-on assignments, and discussions, students will gain insights into both the theoretical underpinnings and practical implementations of AI systems. Topics such as ethical considerations, robustness, and limitations of AI will also be addressed to encourage critical thinking about the role of AI in society.

Prerequisites

There are no formal pre-requisites for the course, but students should have previous programming experience (programming assignments will be given in Python), as well as some general CS background. Please see the instructors if you are unsure whether your background is suitable for the course.

There is no formal textbook for the course. Lectures will provide references to readings as appropriate.

Office Hours

Name	Email	Hours
Aditi Raghunathan	aditirag@cs.cmu.edu	Mondays 3.30 pm - 4.30 pm (while walking from class to GHC 7005 and then at GHC 7005)
Lingjing Kong	lingjink@cs.cmu.edu	Wednesdays 3:30 pm - 4:30 pm (GHC 9115)
Chen Wu	chenwu2@cs.cmu.edu	Tuesday 4:00 pm - 5:00 pm (GHC 9115)

Schedule (Subject to change)

Homework Schedule

HW	Release date	Due date
Homework 1	1/13	1/20
Homework 2	1/27	2/10

Lecture Schedule

Date	Topic	Slides	Readings
1/13	Introduction	Lecture 1	Chapter 1 of "Artificial Intelligence: A Modern Approach", 4th edition, by Russell and Norvig. The bitter lesson 2018 ACM Turing Award
1/15	Supervised machine learning - 1	Lecture 2
1/20	No class (MLK day)
1/22	Supervised machine learning - 2	Lecture 3
1/27	Supervised machine learning - 3
1/29	Optimization - 1
2/3	Optimization - 2
2/5	Neural networks - 1
2/10	Neural networks - 2
2/12	Representation learning, unsupervised learning - 1
2/17	Multimodal models (CLIP)
2/19	Large language models - 1
2/24	Large language models - 2
2/26	Mid term
3/3	No class (spring break)
3/5	No class (spring break)
3/10	Scaling laws and emergent capabilities - 1
3/12	Scaling laws and emergent capabilities - 2
3/17	Inference-time methods + Search - 1
3/19	Search - 2
3/24	CSPs and planning
3/26	MDPs and RL - 1
3/31	MDPs and RL - 2
4/2	Game theory
4/7	Diffusion models - 1
4/9	Diffusion models - 2
4/14	Guest lecture on privacy/security
4/16	Guest lecture on ethics
4/21	Wrap up and AMA
4/23	Poster presentation

Homework Assignments

There will be five assignments: they will involve both written answers and programming assignments. Written questions will involve working through algorithms presented in the class, deriving and proving mathematical results, and critically analyzing the material presented in class. Programming assignments will involve writing code in Python to implement various algorithms presented in class.

Instructions for submitting homework will be added soon.

Homework Policies

• You can use no more than 3 late days per assignment and no more than a total of 8 late days over the semester. These late days can and should be used in the event that something comes up that you did not plan for. You do not need to notify the course staff if you plan to use them. No credit will be given for assignments submitted more than 3 days (72 hours) after the posted deadline.

• You can discuss both the programming and written portions with other students, but all final submitted work (code and writeups) must be done entirely on your own, without looking at any notes generated during group discussions. Be sure to mention your collaborators' names and Andrew IDs in your writeup. The use of generative AI tools is allowed, but you are expected to exercise your own judgment and ensure that you fully understand the required concepts through your engagement with the assignments.

• Can search the internet for references but you are not allowed to post the questions on stackoverflow or anywhere else.

• If you reference any code or sources other than the materials provided on the course website or the textbook, you must mention the source. If you have any questions about whether or not you can use a source, please ask.

Course project

The course project can be completed in groups of 1-3 students and may explore any topic that is at least loosely related to the themes covered in the course. Detailed guidelines and timelines will be provided soon.

Accommodations for Students with Disabilities

If you have a disability and have an accommodations letter from the Disability Resources office, we encourage you to discuss your accommodations and needs with us as early in the semester as possible. We will work with you to ensure that accommodations are provided as appropriate. If you suspect that you may have a disability and would benefit from accommodations but are not yet registered with the Office of Disability Resources, we encourage you to visit their website.

Statement of Support for Students’ Health & Well-being

Take care of yourself. Do your best to maintain a healthy lifestyle this semester by eating well, exercising, getting enough sleep, and taking some time to relax. This will help you achieve your goals and cope with stress.

All of us benefit from support during times of struggle. There are many helpful resources available on campus and an important part of the college experience is learning how to ask for help. Asking for support sooner rather than later is almost always helpful.

If you or anyone you know experiences any academic stress, difficult life events, or feelings like anxiety or depression, we strongly encourage you to seek support. Counseling and Psychological Services (CaPS) is here to help: call 412-268-2922 and visit their website at http://www.cmu.edu/counseling/. Consider reaching out to a friend, faculty or family member you trust for help getting connected to the support that can help.

If you have questions about this or your coursework, please let us know. Thank you, and have a great semester.

Statement of Commitment to a Diverse Learning Environment

We must treat every individual with respect. We are diverse in many ways, and this diversity is fundamental to building and maintaining an equitable and inclusive campus community. Diversity can refer to multiple ways that we identify ourselves, including but not limited to race, color, national origin, language, sex, disability, age, sexual orientation, gender identity, religion, creed, ancestry, belief, veteran status, or genetic information. Each of these diverse identities, along with many others not mentioned here, shape the perspectives our students, faculty, and staff bring to our campus. We, at CMU, will work to promote diversity, equity and inclusion not only because diversity fuels excellence and innovation, but because we want to pursue justice. We acknowledge our imperfections while we also fully commit to the work, inside and outside of our classrooms, of building and sustaining a campus community that increasingly embraces these core values.

Each of us is responsible for creating a safer, more inclusive environment.

Unfortunately, incidents of bias or discrimination do occur, whether intentional or unintentional. They contribute to creating an unwelcoming environment for individuals and groups at the university. Therefore, the university encourages anyone who experiences or observes unfair or hostile treatment on the basis of identity to speak out for justice and support, within the moment of the incident or after the incident has passed. Anyone can share these experiences using the following resources:

• Center for Student Diversity and Inclusion: csdi@andrew.cmu.edu, (412) 268-2150
• Report-It online anonymous reporting platform: reportit.net username: tartans password: plaid

All reports will be documented and deliberated to determine if there should be any following actions. Regardless of incident type, the university will use all shared experiences to transform our campus climate to be more equitable and just.