07-280 AI & ML I

New in Spring 2026!

Overview

This course provides an integrated introduction to artificial intelligence and machine learning that bridges core AI methods with modern approaches. Students develop both theoretical mastery and practical expertise by combining foundational concepts with the construction of influential AI systems.

The curriculum covers foundational materials in search, machine learning, reinforcement learning, and probability. Students then build on these to construct detailed implementations of landmark AI systems such as AlexNet, GPT-2, and AlphaZero. This rigorous approach develops the analytical skills needed to build the future AI. Finally, as an essential component, this course will address the ethics and responsible development of AI/ML technology and products.

The course emphasizes both technical excellence and ethical considerations in AI development. It serves as the foundation for 07-380 Artificial Intelligence and Machine Learning II, which explores advanced topics, research methods, and specialized applications.

Office Hours

The calendar of office hours (and recitations) is below.

OHQueue: Link

Instructor OH by Appointment

To meet with Nihar, please send him an email with the course number in the subject line a brief topic of discussion in the body. In addition to Pat's standing office hours on the calendar above, Pat often has "OH" (or "Open") appointment slots on his office hours appointment calendar. If no there are no available OH or appointments that meet your needs, please contact Pat via a private post on Piazza with a list of times that work for you to meet.

Schedule

Subject to change

Textbooks:

(AIMA) Russell, Stuart and Peter Norvig. Artificial Intelligence: A Modern Approach, 4th Edition, available via CMU Library

Bishop, Christopher. Pattern Recognition and Machine Learning, available online

Daumé III, Hal. A Course in Machine Learning, available online

(DL) Goodfellow, Ian, Yoshua Bengio, Aaron Courville. Deep Learning, available online

(MML) Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong. Mathematics for Machine Learning, available online

Mitchell, Tom. Machine Learning, available online

Murphy, Kevin P. Machine Learning: A Probabilistic Perspective, available online

(KMPA) Shaw-Taylor, John, Nello Cristianini. Kernel Methods for Pattern Analysis, available online

Dates	Topic	Lecture Materials (or nearest neighbor pdf prior to lecture)	Pre-Reading	Reading (optional)
1/13 Tue	1. Introduction	pptx pdf Notation_Guide.pdf Math_Background.pdf	Search Pre-reading Checkpoint due 1/14 Wed, 11:59 pm	MML 2.1-3, 2.5, 2.6 and 3.1, 3.2.1, 3.3
1/15 Thu	2. Heuristic Search	pptx (inked) pdf (inked)		AIMA Ch. 3.1-6
1/20 Tue	3. Adversarial Search	pptx (inked) pdf (inked)	Adversarial_Search.pdf Checkpoint due 1/19 Mon, 11:59 pm	AIMA Ch. 5.1-2, 5.5
1/22 Thu	4. Constraint Satisfaction Problems	pptx (inked) pdf (inked) CSP Demo		AIMA Ch. 6.1-3, 6.5
1/27 Tue	5. ML Problem Formulation	ML Problem Formulation.pdf	Decision Trees.pdf Checkpoint due 1/26 Mon, 11:59 pm	Mitchell 1.1-1.2 Daumé 1 10-315 S25: pptx (inked) pdf (inked)
1/29 Thu	6. Decision Trees	Decision Trees.pdf		Daumé 2 Entropy, Cross-Entropy video, A. Géron Paper: ID3 10-315 S25: pptx (inked) pdf (inked)
2/3 Tue	7. Linear Regression	Linear Regression.pdf	Opt and LinReg.pdf Checkpoint due 2/3 Tue, 10:00 am (before lecture)	MML 8.2-8.2.2, 8.2.4 MML 5.2-5.5 10-315 S25: pptx (inked) pdf (inked) regression interactive.ipynb regression blind interactive.ipynb
2/5 Thu	8. Optimization	Optimization.pdf
2/10 Tue	9. Logistic Regression	Logistic Regression.pdf	FeatEng and LogReg.pdf Checkpoint due 2/9 Mon, 11:59 pm	Bishop 4.3.2, 4.3.4 S25 10-315: pdf Demos: Linear/Logistic: notebook, Desmos (3D) Convex functions Desmos Multiclass logistic Desmos
2/12 Thu	10. Feature Engineering and Regularization	Model Selection.pdf		MML 8.3.3 DL 7.1,7.8 Bishop 3.1.4 S25 10-315: pdf, pdf, and pdf Demos regression regularization.ipynb Regularization interpolation Desmos (3D) L1_sparsity.ipynb Quadratic Logistic notebook, Desmos 3D
2/17 Tue	11. Neural Networks	pptx (inked) pdf (inked) Three Neuron Interactive Tensorflow Playground (regression)	Neural Networks.pdf Checkpoint due 2/16 Mon, 11:59 pm	DL 6
2/19 Thu	12. Neural Networks (cont.)	pptx (inked) pdf (inked) Universal network Desmos Perceptron neuron Desmos		MML 5.6 The Matrix Cookbook
2/24 Tue	Midterm Exam 1 In-class	Learning objectives: pdf
2/26 Thu	13. AI Alignment	AI Alignment.pdf Autonomous Scientists.pdf		Model Cards For Model Reporting. Margaret Mitchell, et al (2019)
3/3 Tue	No class: Spring Break
3/5 Thu	No class: Spring Break
3/10 Tue	14. Computer Vision	Computer Vision.pdf	CNNs.pdf Checkpoint due 3/9 Mon, 11:59 pm	DL 9 S25 10-315: pptx (inked) pdf (inked)
3/12 Thu	15. Pre-training/Transfer Learning/Fine-tuning	Pre-training/Transfer Learning/Fine-tuning.pdf		PyTorch Basics Tutorial
3/17 Tue	16. MLE and Probabilistic Modeling	MLE.pdf	MLE.pdf Checkpoint due 3/16 Mon, 11:59 pm	MML 9-9.2.2 Bishop 1.2.4-5, 3.1.1-2 S25 10-315: pptx (inked) pdf (inked)
3/19 Thu	17. MLE (cont.) & Natural Language Processing	NLP: pptx (inked) pdf (inked) N-grams Worksheet: pptx (sol) pdf (sol)
3/24 Tue	18. Markov Chains, N-grams	pptx (inked) pdf (inked) Demo: Find N-grams Demo: N-grams	Word Embeddings.pdf Checkpoint due 3/23 Mon, 11:59 pm
3/26 Thu	19. Feature Learning, Word Embedding	pptx (inked) pdf (inked) Demos: Cosine Similarity Word Embeddings		The Illustrated Word2vec. Jay Alammar
3/31 Tue	20. Attention, Transformers, LLMs	pptx (inked) pdf (inked) Demos: Position Encoding 2D Linear Transforms Linear Transforms (words) Attention		The Illustrated { Attention → Transformer → GPT-2 }. Jay Alammar Video (and code): Let's build GPT. Andrej Karpathy Demo: Rotary Position Encoding 2D
4/2 Thu	21. Markov Decision Processes	pptx (inked) pdf (inked)	MDP.pdf Checkpoint due 4/1 Wed, 11:59 pm	AIMA Ch. 17.1-2
4/7 Tue	22. Reinforcement Learning	pptx (inked) pdf (inked)	No checkpoint due this week	AIMA Ch. 22.1-4.3
4/9 Thu	No class: Carnival
4/14 Tue	23. Deep Reinforcement Learning	10-301/601 S25: pdf	Approx Q-learning.pdf Checkpoint due 4/13 Mon, 11:59 pm	AIMA Ch. 22.4, 22.7 Playing Atari with Deep Reinforcement Learning Mnih, et al, 2013. Human-level control through deep reinforcement learning Mnih, et al, 2015.
4/16 Thu	24. Monte Carlo Tree Search	10-403 S26: pdf dropbox		AIMA Ch. 5.4 A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play Silver, et al, 2018.
4/21 Tue	Midterm Exam 2 In-class	Learning objectives: pdf
4/23 Thu	25. LLM Post Training	pdf
	Study n'at
5/4 Mon	Final Exam GHC 4401 (Rashid)	Full course learning objectives: pdf

Recitation

Recitation starts the first week of class, Friday, Jan. 16th. Recitation attendance is recommended to help solidify weekly course topics. That being said, the recitation materials published below are required content and are in scope for midterms 1 and 2. Students frequently say that recitations are one of the most important aspects of the course.

Recitation section assignments will be locked down after the third week. Until then, you may try attending different recitation sections to find the best fit for you. In the case of any over-crowded recitation sections, priority goes to students that are officially registered for that section in SIO. The process to select your final recitation assignment will be announced on Piazza as we get closer to Recitation 4.

Recitations will be on Fridays in the following individual recitation sections:

Section	Time	Location	TAs	Resources
A	Friday 10:00 am - 10:50 am	WEH 4708	Max & Roy	Drive folder
B	Friday 11:00 am - 11:50 am	WEH 4708	Johnny & Eungyeup	Drive folder
C	Friday 12:00 pm - 12:50 pm	DH 1117	Ellyse & Sunny	Drive folder
D	Friday 1:00 pm - 1:50 pm	GHC 4211	Steven & Avi	Drive folder
E	Friday 2:00 pm - 2:50 pm	WEH 4708	Steven & Avi	Drive folder

Dates	Recitation	Handout/Code
CMU-Qatar	Recitation 0: Numpy	pptx pdf (recording) NumPy_tutorial.ipynb NumPy_Rec0_Practice.ipynb (solution)
1/16 Fri	Recitation 1: Search	Worksheet: pdf (solution)
1/23 Fri	Recitation 2: Adversarial Search & CSPs	Worksheet: pdf (solution)
1/30 Fri	Recitation 3: Decision Trees	Worksheet: pdf (solution)
2/6 Fri	Recitation 4: Matrix Calc & Linear Regression	Worksheet: pdf (solution)
2/13 Fri	Recitation 5: Logistic Regression & Regularization	Worksheet: pdf (solution)
2/20 Fri	Recitation 6: Neural Networks	Worksheet: pdf (solution)
2/27 Fri	Recitation 7: PyTorch	Slides, Notebook
3/6 Fri	No recitation -- Spring Break
3/13 Fri	Recitation 8: CNNs	Worksheet: pdf (solution)
3/20 Fri	Recitation 9: MLE & Tutorial on LaGrange Multipliers	Worksheet: pdf (solution)
3/27 Fri	Recitation 10: Word Embeddings	Worksheet: pdf (solution)
4/3 Fri	Recitation 11: Attention and MDPs	Worksheet: pdf (solution)
4/10 Fri	Recitation 12	Worksheet: pdf (solution)
4/17 Fri	Recitation 13: Approx. Q-learning + MCTS	Worksheet: pdf (solution)
4/24 Fri	Recitation 14	Worksheet: pdf (solution)

Assignments

There will be twelve assignments with three possible components each: programming/written/online (subject to change). Written/online assignments will involve working through algorithms presented in the class, deriving and proving mathematical results, and critically analyzing material presented in class. Programming assignments will involve writing code in Python to implement various algorithms.

There will also be weekly pre-reading assignments and associated checkpoints.

For any assignments that aren't released yet, the dates below are tentative and subject to change.

Homework due dates (Tentative)

Note: Links will be added to the table when assignments are released.

Homework	Link (if released)	Due Date
HW0 (online)	Online	1/15 Thu, 11:59 pm
HW1 (online, written, programming)	Online hw1.pdf, hw1_tex.zip hw1.ipynb	1/22 Thu, 11:59 pm
HW2 (online, written, programming)	Online hw2.pdf, hw2_tex.zip HW2 Programming	1/29 Thu, 11:59 pm
HW3 (online only)	Online	2/5 Thu, 11:59 pm
HW4 (written only)	hw4.pdf, hw4_tex.zip,	2/12 Thu, 11:59 pm
HW5 (mostly programming)	Online hw5.pdf, hw5_tex.zip hw5.ipynb	2/19 Thu, 11:59 pm
HW6 (mostly written)	hw6.pdf, hw6_tex.zip,	2/26 Thu, 11:59 pm
HW7 (mostly programming)	hw7.pdf, hw7_tex.zip hw7.ipynb	3/12 Thu, 11:59 pm
HW8 (mostly programming): Building AlexNet	Online hw8.pdf, hw8_tex.zip hw8_v0_3_1.ipynb	3/19 Thu, 11:59 pm
HW9 (online, written, programming)	Online hw9.pdf, hw9_tex.zip hw9.ipynb	3/26 Thu, 11:59 pm
HW10 (online, programming)	Online hw10.ipynb	4/2 Thu, 11:59 pm
HW11 (online, programming, written): Building GPT2	Online hw11.pdf, hw11_tex.zip hw11[GPT].ipynb HW11[RL] Programming	4/16 Thu, 11:59 pm
HW12 (online, programming): Building AlphaZero	Online hw12.ipynb	4/24 Fri, 11:59 pm

Pre-reading due dates (Tentative)

Pre-reading	Link (if released)	Due Date
PR1: Search	Checkpoint	1/14 Wed, 11:59 pm
PR2: Adversarial Search	Checkpoint	1/19 Mon, 11:59 pm
PR3: Decision Trees	Checkpoint	1/26 Mon, 11:59 pm
PR4: Optimization and Linear Regression	Checkpoint	2/3 Tue, 10:00 am (before lecture)
PR5: Feature Engineering and Logistic Regression	Checkpoint	2/9 Mon, 11:59 pm
PR6: Neural Nets	Checkpoint	2/16 Mon, 11:59 pm
No PR7	---	---
PR8: CNNs	Checkpoint	3/9 Mon, 11:59 pm
PR9: MLE	Checkpoint	3/16 Mon, 11:59 pm
PR10	Checkpoint	3/23 Mon, 11:59 pm
PR11	Checkpoint	3/30 Mon, 11:59 pm
PR12	No Pre-reading this week	N/A
PR13	Released 4/8 Wed	4/13 Mon, 11:59 pm
No PR14	---	---

Course Notes

In addition to the pre-reading notes, below are course notes on some of the topics developed by the course staff over the years.

Exams

The course includes two midterm exams and a final exam. The midterms will take place in lecture on Tue, Feb 24 and Tue, Apr 21. The final exam date will be announced by the university mid-semester. The final exam could be as late as Mon, May 4 and the makeup exam date is Tue, May 5. Plan any travel around exams, as exams will not be rescheduled.

Policies

Grading

Grades will be collected and reported in Canvas. Please let us know if you believe there to be an error the grade reported in Canvas.

Final scores will be composed of:

• 15% Midterm exams (each)
• 25% Final exam
• 30% Programming/Written homework
• 5% Online homework
• 5% Pre-reading checkpoints
• 5% Participation

Final grade

This class is not curved. However, we convert final course scores to letter grades based on grade boundaries that are determined at the end of the semester. What follows is a rough guide to how course grades will be established, not a precise formula — we will fine-tune cutoffs and other details as we see fit after the end of the course. This is meant to help you set expectations and take action if your trajectory in the class does not take you to the grade you are hoping for. So, here's a rough heuristics about the correlation between final grades and total scores:

• A: above 90%
• B: 80-90%
• C: 70-80%
• D: 60-70%

This heuristic assumes that the makeup of a student's grade is not wildly anomalous: exceptionally low overall scores on exams, programming assignments, or written assignments will be treated on a case-by-case basis and, while rare, could potentially drop a student's grade.

Precise grade cutoffs will not be discussed at any point during or after the semester.

Participation

In class, we will use a series of polls as part of an active learning technique called Peer Instruction. Your participation grade will be based on the percentage of these in-class poll questions answered:

• 0% for 50% or less
• 5% for 80% or greater poll participation
• Linear scale for values in between 50% and 80%

• Correctness of in-class polling responses will not be taken into account for participation grades.
• If a poll is duplicated with the same question (e.g. before and after discussing with your neighbor), you should answer all of the duplicated versions as well, as they will be counted as separate polls.
• If you have systemic/repeated technical issues, please let us know as soon as possible, so we can resolve the situation.
• Missing polls due to absences (e.g., brief illness) from lecture or due to technical difficulties is expected occasionally, and this is why you only need to answer >= 80% of the polls to get full credit.

It is against the course academic integrity policy to answer in-class polls when you are not present in lecture. Violations of this policy will be reported as an academic integrity violation. Information about academic integrity at CMU may be found at https://www.cmu.edu/academic-integrity.

Late Policies, and Extensions, and Exceptions

Participation

• Missing polls due to absences (e.g., brief illness) from lecture or due to technical difficulties is expected occasionally, and this is why you only need to answer >= 80% of the polls to get full credit.
• If you must miss many lectures due to circumstances outside of your control (e.g., if you have an extended illness) please e-mail Brynn, bedmunds@andrew.cmu.edu, prior to lecture.

Pre-reading checkpoints

Pre-reading checkpoints don't have any extensions or late days. However, the lowest two checkpoints will be dropped when computing your semester score. Reasoning: We want to make sure that everyone is able to complete the pre-reading prior to lecture, so we can build on that knowledge in class; minor illness and other minor disruptive events outside of your control happen occasionally and thus dropping the lowest two scores. See below for information on rare exceptions.

Late days for written, programming, and online homework

You have a pool of 6 late days across all written, programming and online assignment types

• Use up to two per assignment number
• Components of an assignment with the same homework number are considered the same assignment; so e.g., if you turn in both programming and written components within 24 hours after the due date, you will use one late day, not two.
• You may use these at your discretion, but they are intended for minor illness and other disruptive events outside of your control, and not for poor time management.
• No need to inform us that you are using a late day; just submit it to Gradescope during the late day.
• You are responsible to keep track of your own late days. Gradescope will not enforce the total number of late days
• Homework submitted after these two late days or submitted by a student without any late days remaining will be given a score of 0.

Exceptions and extensions

Aside from late days, dropping the lowest checkpoints, and the 80% threshold for participation, there will be no extensions on assignments in general. If you think you really really need an extension on a particular assignment, e-mail Brynn, bedmunds@andrew.cmu.edu, as soon as possible and before the deadline. Please be aware that extensions are entirely discretionary and will be granted only in exceptional circumstances outside of your control (e.g., due to severe illness or major personal/family emergencies, but not for competitions, club-related events, or interviews). The instructors will require confirmation from your academic advisor, as appropriate.

We certainly understand that unfortunate things happen in life. However, not all unfortunate circumstances are valid reasons for an extension. Nearly all situations that make you run late on an assignment homework can be avoided with proper planning - often just starting early. Here are some examples:

• I have so many deadlines this week: you know your deadlines ahead of time - plan accordingly.
• It's a minute before the deadline and the network is down: you always have multiple submissions - it's not a good idea to wait for the deadline for your first submission.
• My computer crashed and I lost everything: Use Google Drive, Dropbox, or similar system to do real-time backup - recover your files and finish your homework from a cluster machine or borrowed computer.

Collaboration Policy

We encourage you to discuss course content and assignments with your classmates. However, these discussions must be kept at a conceptual level only. You may use generative AI tools to better learn course content, but they may not be used to generate any part of your assignment submission. (Yes, these tools are awesome and you should learn how to use them well.

• You may NOT view, share, or communicate about any artifact that will be submitted as part of an assignment. Example artifacts include, but are not limited to: code, pseudocode, diagrams, and text.
• You may look at another student's Python error messages and discuss what the error means at a conceptual level. However, you may NOT give specific instructions to fix the error.
• All work that you present must be your own. Thus, generative AI tools may not be used to generate any part of your assignment submission.
• Using any external sources of code or algorithms in any way must have approval from the instructor before submitting the work. For example, you must get instructor approval before using an algorithm you found online for implementing a heuristic function in a programming assignment. The following are pre-approved exceptions:
  • Code provided by the course staff or materials, including code from the textbook
  • Documentation for permitted Python libraries

Programming component partners

The only exception to the above collaboration policy is when you share programming code directly with your programming assignment partner.

• You are allowed to work in groups of at most two on any programming componenet of each assignment, i.e. you and one partner
• You must still do ALL parts of written and online components on your own, including the written results and analysis from the programming component. (Naturally, you would have the same result figures if generated from the programming component).
• You must specify your partner when you submit the programming component of each assignment
• Once you start working with a partner on an assignment, you may not switch to another partner for that assignment
• You may change partners between assignments. In fact, you are strongly encouraged to change partners occasionally
• Important: You are responsible for making sure that both you AND your partner understand the work that you submit. If we discover that one partner cannot answer basic questions about the submitted work, both students in the group will be reported for an academic integrity violation.

Violations of these policies will be reported as an academic integrity violation. Information about academic integrity at CMU may be found at https://www.cmu.edu/academic-integrity. Please contact the instructor if you ever have any questions regarding academic integrity or these collaboration policies.

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.

Frequently Asked Questions

• How does this compare to 10-301?
• Why this course?
• Will 15-281 and 10-315 continue to be offered?
• Who will teach the new courses?
• How often will 07-280 and 07-380 be offered?
• What topics will be covered in 07-380 AI & ML II?
• Are the prereq and coreqs strict requirements?

Question not answered here? Please fill out this form.

How does this compare to 10-301?

Both courses cover sufficient material for an intro machine learning course 07-280 includes non-ML AI techniques, while 10-301 focuses only on ML, naturally reaching a few additional ML topics.

	07-280	10-301
Prereqs (see course description for detailed course numbers)	Prereq: 15-122 Coreq: Probability Prereq: Linear Algebra Prereq: 15-151/Concepts Coreq: Calc 2	Prereq: 15-122 or 15-121 Prereq: Probability Prereq: (Linear Algebra or Calc 3 or 151/Concepts)
Fulfills the Intro ML prereq for later ML (10-XXX) courses	check_circle	check_circle
Fulfills the 07-280 prereq for 07-380 AI/ML II	check_circle	No, but if you've already taken 10-301 (and thus can't take 07-280), email bsai@cs.cmu.edu to discuss alternative pathways for taking 07-380.
Fulfills the 07-280 requirement for the AI Major, Additional Major, and Minor	check_circle	No, but if you've already taken 10-301 (and thus can't take 07-280), email bsai@cs.cmu.edu to discuss alternative pathways for satisfying the AI Core requirements.
Fulfills the AI elective for the Computer Science Major and Additional Major	check_circle	check_circle
Fulfills the Intro ML requirement for the Stat/ML Major	check_circle	check_circle
Fulfills the Intro ML prereq for the ML Concentration Minor	check_circle	check_circle
Fulfills the Intro ML prereq for the 5th year ML Master's	check_circle	check_circle
Topics: ML fundamentals from decision trees to neural networks	check_circle	check_circle
Topics: Transformer networks and Large Language Models	check_circle	check_circle
Topics: Reinforcement Learning	check_circle	check_circle
Additional topics: Heuristic Search Adversarial Search Constraint Satisfaction Problems ML Parallelism/GPU Basics Monte Carlo Tree Search	check_circle
Additional topics: k-Nearest Neighbors Perceptron Algorithm ML Theory: PAC Learning PCA Clustering and K-means Ensemble Methods: Bagging and Boosting Recommender Systems Maximum a Posteriori		check_circle
TA mascot	🤷	Neural the Narwhal

Why this course?

The goal is to replace the older AI and ML courses, 15-281 and 10-315, with two sequenced courses, 07-280 and 07-380, covering the breadth and depth required by the AI majors, with the first of the two courses covering core AI and ML concepts for SCS students taking only one AI course, as well as anyone at CMU who wants a good technical introduction to the field.

This restructure will provide the following benefits:

• Flexibility to grow two AI courses
  • Adapting topics
  • Building on first course in the second course
• Better single AI course for non-AI majors
  • First course as accessible as 15-281 is now
  • First course includes core ML topics in addition to AI breadth

Will 15-281 and 10-315 continue to be offered?

No, 15-281 and 10-315 are being retired and will not be offered in the future.

Who will teach the new courses?

The new courses will be taught by a mix of faculty, primarily from the Machine Learning and Computer Science Departments.

In Spring 2026, 07-280 will be taught by Nihar Shah (CSD/MLD) and Pat Virtue (CSD/MLD), and 10-301 will be taught by Matt Gormley (MLD) and Pat Virtue (CSD/MLD).

How often will 07-280 and 07-380 be offered?

Both courses, 07-280 and 07-380, will be offered every semester (Fall and Spring), with 07-380 first being offered in Fall 2026.

What topics will be covered in 07-380 AI & ML II?

07-380 is designed to be more flexible in its topics from semester to semester, adapting based on our faculty's best understanding of what additional/advanced AI/ML topics students need to learn, especially those graduating with a major/minor in AI. It builds upon 07-280, so we'll be able to explore more advanced topics in greater depth, while also increasing the breadth of topics across all of AI.

Potential topics include: Deeper AI/ML Ethics, MAP, ML Theory: PAC Learning, PCA, Clustering and K-means, Ensemble Methods: Bagging and Boosting, Recommender Systems, Linear programming, Integer programming, Propositional Logic, SAT, and Logical Agents, Classical Planning, Bayes' Nets: Representation, Bayes' Nets: Inference, Bayes' Nets: Sampling, HMMs, Game Theory: Equilibrium, Game Theory: Social Choice, Vision Transformers, Variational Autoencoders, Diffusion, Text to Image Generation, Distributed Deep Learning, Optimization: RMS, Momentum, Stability, RLHF and DPO.

Are the prereq and coreqs strict requirements?

Yes, the prerequisites and corequisites are strict requirements for enrollment in 07-280.