Sequence Models, Seq2Seq, and Transformers | Berkeley CS288 Topic Guide
Units 05–07 move from recurrent state to encoder-decoder models, then rewrite the information path with attention and Transformer blocks.
learning
345 postsAdvanced AI and Machine Learning: From Logical Planning to Generative Models | CMU 07-380 Course Overview
07-380 Fall 2026 is the first offering of CMU's new AI II: 26 lectures from logic, planning and optimization to probabilistic graphs and generative systems. As of the 2026-09-29 course site, the Lec1–9 slides, PR1–6 notes, Rec1–5 (with solutions) and HW1–3 are public, and this site now has 14 lecture-by-lecture guides for them. Slides after Lec10, HW4–7 and the final project are not out yet, so the course as a whole is still A2.
Find ML Foundation Gaps with Mathematics and Python | CMU 10-301 Assignment 1
HW1 is written and programming work: mathematical and CS foundations followed by a majority-vote classifier.
From Information Calculations to a Complete Decision Tree | CMU 10-301 Assignment 2
HW2 moves from hand-calculated entropy and mutual information to an end-to-end tree learner, predictor, and evaluator.
Compare K-NN, Perceptron, and Linear Regression | CMU 10-301 Assignment 3
HW3 is written work: a decision-tree review followed by K-NN, Perceptron, and Linear Regression through inductive bias, errors, and model selection.
Turn Logistic Regression Likelihood into a Classifier | CMU 10-301 Assignment 4
HW4 joins probabilistic interpretation, cross-entropy gradients, and implementation into one traceable training pipeline.
Expose Neural Networks and Backpropagation with NumPy | CMU 10-301 Assignment 5
HW5 avoids automatic differentiation so learners must track forward shapes, caches, and backward gradients themselves.
Learning Theory, MLE/MAP, and Fairness Metrics | CMU 10-301 Assignment 6
HW6 combines generalization, MLE/MAP, probabilistic learning, fairness metrics, and social impact in one written assignment about assumptions and tradeoffs.
Move from Basic Neural Networks to Deep Learning | CMU 10-301 Assignment 7
HW7 builds on HW5 backpropagation to address deep-model architecture and training failures, emphasizing diagnosis over merely adding layers.
From MDPs to Reinforcement-Learning Updates | CMU 10-301 Assignment 8
HW8 connects states, actions, rewards, transitions, and value updates while separating environment dynamics, policy, and estimation error.
Close the Course with Ensembles, k-Means, PCA, and Recommenders | CMU 10-301 Assignment 9
The final written assignment combines ensembles, clustering, representation, and recommendation to test whether you can choose a learning paradigm from problem structure.
Learn Machine Learning Through Nine Assignments | CMU 10-301 Course Overview
Spring 2026 publishes material for 27 lectures and nine homework bundles; outsiders can do the core work but cannot access Panopto, Piazza, Gradescope, or official homework solutions.
From AI Foundations to Machine Learning: Learning Paths through CS50 AI, CS181, and CS182 | Global AI/CS Course Map
CS50 AI is Harvard's most complete public entry point, but the Summer 2026 course still uses 2020 recordings and assignment assets while the rolling OCW projects have moved to other editions. CS181 Spring 2026 exposes current homework and notes without current recordings; CS182 Fall 2026 has not yet completed an offering.
Berkeley AI/ML Course Guide: From CS61A to CS288, What Can You Actually Study Online? | Global AI/CS Course Map
Berkeley has no standalone undergraduate AI degree. A workable path builds on the CS BA or EECS BS foundation, enters through either CS188's broad AI curriculum or CS189's mathematical machine learning curriculum, then branches into deep learning, NLP, vision, or reinforcement learning. Many 2025–2026 courses are A3, but the newest class, the newest stable URL, and the best self-study edition are not always the same.
CMU AI/ML Course Guide: The New 07-280 Core and a Public Self-Study Route | Global AI/CS Course Map
CMU's current BSAI now runs through 07-280 and 07-380 before branching into an NLP/vision core and four AI clusters. 07-380 debuted in Fall 2026, and the same semester launched a graduate-level 11-768 AI Agents course. The residual Spring 2026 materials for 07-280 and the complete 10-301/601 site already support self-study; retired 15-281 remains a useful legacy route.
A Global Map of AI and CS Courses: Which Ones Can You Actually Study in Public? | Global AI/CS Course Map
This map audits AI and CS courses at Stanford, CMU, MIT, UC Berkeley, Harvard, and National Taiwan University (NTU) in 2025–2026 using four access labels: A0 for a visible catalog entry, A1 for a public syllabus, A2 for partial materials, and A3 for a self-study-ready package. A course site or YouTube playlist can exist without giving outsiders access to the current videos, assignments, or starter code.
From Programming and Mathematics to AI: A Learning Path through Open Courses | Global AI/CS Course Map
MIT has offered Course 6-4, a formal BS in Artificial Intelligence and Decision Making, since 2022. For an outside learner, however, the current degree requirements, the 2025–2026 course sites, and the best OCW editions rarely line up. A workable route follows 6-4's programming, algorithms, linear algebra, and probability foundation, then selects among 6.S191, 6.3900, 6.4110, 6.7960, vision, and robotics according to what is actually public.
A Reading Guide to Stanford's CS Courses: Ordered by Prerequisites, from CS106A to CS336 | Stanford's Main-Line CS Courses Course Map
Stanford CS rests on CS103, CS107, CS109, CS111, and CS161; CS221 names three of those plus CS106B as preparation. This guide combines official prerequisites with an explicitly editorial reading order and marks public-material and offering risks.