Final Projects: Build a Reproducible Generative-Model Experiment | CMU 10-423 Topic Guide
Design controlled comparisons, retain paired outcomes, and distinguish replication from working code in a generative-model project.
#self-study
27 postsPaper Reports: Turn a Model Summary into a Testable Argument | CMU 10-423 Assignment 623
Separate paper claims, empirical support, and your own interpretation through a role-based report and a controlled-comparison example.
Practice Exam: Diagnose Conceptual Gaps | CMU 10-423 Topic Guide
Use the public practice exam to diagnose assumptions, data flow, and evaluation errors through a worked probability example.
AI Engineering from Scratch: A Guide to the Free Course That Builds Everything by Hand, from Linear Algebra to Multi-Agent Systems
AI Engineering from Scratch is a free, MIT-licensed course on GitHub with about 63k stars: 20 phases and 523 lessons, running from linear algebra, classical ML, and deep learning to LLMs, agents, and AI safety. Each lesson builds the algorithm by hand, then repeats it with a framework. The early math and deep-learning-core phases are worth following step by step; skip the Phase 10 SFT/RLHF/DPO lessons, and treat the rest as a map of topics.
Exams, Papers, and Projects: Three Forms of Learning Evidence | CMU 10-423 Topic Guide
An exam tests reasoning under a fixed question. A paper report tests your judgment of someone else’s evidence. A project tests your ability to produce reproducible evidence. These are distinct learning tasks, now taught separately while this URL remains an assessment map.
A Generative AI Learning Path: Reading the 35 Core Guides | CMU 10-423 Course Overview
This series uses the public Spring 2026 materials for CMU 10-423/623/723 Generative AI.
LLM Systems: A Learning Path through Training, Inference, and Deployment | CMU 11-868 Course Overview
CMU 11-868 is Lei Li's graduate course on LLM systems: it goes from CUDA kernels and your own MiniTorch framework to distributed training, SGLang serving, and RLHF. All 28 Spring 2026 slide decks, 7 assignment pages, and 7 starter-code repos are public, which rates it A3. What's missing: videos, GPUs and a PSC account, the quizzes, and any official statement of which two assignments are optional.
Transformers and Large Language Models: Nine Lectures from Transformers to AI Agents | Stanford CME295 Course Overview
CME295 is a two-unit Stanford course with no homework; your grade is the midterm and the final, 50% each. The 2025 edition's nine lectures are fully public: videos, slides, and both exams with solutions. The 2026 edition rewrites the agent lecture around context compaction, harnesses, coding agents, and skills, and adds three full lectures on LLM systems, reinforcement learning, and Diffusion LLMs.
An Agents Course You Can't Take Without Having Trained a Language Model, With Three Assignments From Harness to Eval to RL | CMU 11-768 Course Overview
CMU 11-768 is a new Fall 2026 graduate course on agents taught by Graham Neubig and Daniel Fried. The prerequisite — prior experience training language models — is strictly enforced. Its 23 lectures run from tool calling, context, memory, and planning through SFT, RL, sandboxing, and human-agent interaction. Three individual assignments in the first half build a harness, an evaluation, and a training pipeline; the second half is a team research project. Slides and the first ten lecture videos are public.
Should You Rent a GPU to Learn Model Training? GPUtw.ai, LoRA, Jupyter, and the First Experiment
GPUtw.ai makes sense as a short-rental GPU learning tool: start with Jupyter, Ollama, or ComfyUI, then try LoRA/QLoRA on a small model. It is not a large foundation-model training platform, and the first run should verify deployment, billing, and data retention with a small budget.
Completing CMU 07-280: What You Know, What Is Missing, and What Comes Next | CMU 07-280 Course Map
Finishing 07-280 means more than reading 24 guides: produce a search engine, supervised-model comparison, CNN/GPT-2 experiments, and a small RL-plus-MCTS system before choosing 07-380, 10-301, or a specialist course.
The CPU, GPU, and H100 Boundary | Berkeley CS285 Topic Guide
Five assignments move from CPU-friendly imitation learning to H100-based LLM RL and six-hour offline-RL runs; self-learners should use three compute tiers instead of copying the entire enrolled workflow.
Imitation Learning, Distribution Shift, and RL Basics | Berkeley CS285 Lectures 1–4
The first four lectures move from behavioral cloning to MDPs; HW1 turns distribution shift into an observable failure through MSE policies, DAgger, and flow matching.
Deep Reinforcement Learning: From Decision Concepts to Policy Learning and Experiments | Berkeley CS285 Course Overview
Spring 2026 CS185/285 publishes slides for 25 lectures, nine discussion units, five assignments, and starter code; lecture recordings are public on the RAIL YouTube channel, but Ed, Gradescope, and office hours remain enrolled-only, while HW4 defaults to an H100, so this is not a zero-cost open course.
Language Models and NLP: From Foundational Representations to Retrieval and Agents | Berkeley CS288 Course Overview
CS288 moves from n-grams to RAG, reasoning, and agents through 18 public slide units and three assignments; Berkeley-only recordings make this an A3 materials route, not a public video course.
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 the Course Map to the Unix Command Line | Stanford CS107 Lecture 1
Winter 2026 opens by explaining why CS107 goes below programming-language abstractions: from bytes and memory through assembly and heap allocators. It then lays out the 40/10/20/30 grading structure and closes with a first tour of the Unix command line.
From Linear Regression to Training Reasoning Models: A Course Map and Reading Routes | Stanford CS229 Course Overview
CS229 is the Stanford course that works through the mathematical derivations of machine learning. The Spring 2026 recordings make 17 lectures public: the first ten cover linear models, neural networks, and unsupervised learning, and the last seven go on to diffusion models, LLMs, and training reasoning models with reinforcement learning. This series has one post per lecture, and this post is the map: what each lecture actually teaches, where the video titles don't match, and where to pick up what the lecture notes cover but the lectures skipped.
Language Modeling from Scratch: Five Assignments and a GPU Budget You Work Out Yourself | Stanford CS336 Course Overview
CS336 is Stanford's course on building a language model from scratch: five assignments cover the tokenizer and Transformer, training-systems optimization, scaling laws, data processing, and alignment and reasoning RL, with no code skeleton, only tests. The lectures, recordings, all five assignment repos and the leaderboards are public; from Assignment 2 on you supply your own compute, and the course-sponsored compute and grading are not available.
CMU's AI Degrees: The First U.S. AI Bachelor's Turned 'What Should AI Students Learn?' into Graduation Requirements
Stanford has no AI degree; AI is a track inside CS. CMU launched the first U.S. B.S. in Artificial Intelligence in 2018, divided AI into four clusters, required one course from each, and made ethics a graduation requirement. At the master's level, MSAII sits not in CS but in the Language Technologies Institute; 84 of its 195 units cover an innovation process ending in a fundable capstone. Two official-page conflicts emerged during verification: whether the AI Core has two or three courses, and whether MSAII totals 192 or 195 units.
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.
Mathematical Foundations of Computing: How to Prove, Then What Can't Be Proven | Stanford CS103 Course Overview
CS103 is Stanford's mathematical foundations of computing course: the first half teaches proof writing through sets, logic, graphs and induction, and the second half covers finite automata, Turing machines, decidability and P vs NP. Slides, problem sets, starter files and the homegrown handouts are all public, and the autograded problems can be checked locally; solutions, practice exams and lecture recordings sit behind Stanford login.
Computer Organization and Systems: From C and Pointers to x86-64 and Writing Your Own malloc | Stanford CS107 Course Overview
CS107 is Stanford's computer systems course: it goes from bits, C strings and pointers to x86-64 assembly and the runtime stack, and ends with writing your own heap allocator, across seven assignments. Lecture slides, assignment specs and worked lab solutions are public; the assignment starter code lives on Stanford's AFS and is out of reach without a campus account.
Probability for Computer Scientists: From Counting and Inference to Machine Learning | Stanford CS109 Course Overview
CS109 is Stanford's probability course for computer scientists: it runs from counting, conditional probability and random variables to inference, maximum likelihood estimation, logistic regression and deep learning. The interactive course reader and the in-class worksheets with answer keys are public, and every lecture comes with an official LLM learning guide; slides after Lecture 15, the problem-set system and the recordings are closed to outsiders.
Operating Systems Principles: From Threads and Virtual Memory to File Systems | Stanford CS111 Course Overview
CS111 is Stanford's operating systems principles course, organized into concurrency, memory management and file systems, with nine assignments. All 28 lecture handouts, the nine assignment specs and three years of midterms and finals with solutions are public; the starter code requires a login on Stanford's myth machines, and the recordings are on Canvas.
Design and Analysis of Algorithms: From Divide and Conquer and Graphs to Dynamic Programming and Max Flow | Stanford CS161 Course Overview
CS161 is Stanford's design and analysis of algorithms course: eighteen lectures go from asymptotic analysis, divide and conquer and randomized algorithms to graph algorithms, dynamic programming, greedy algorithms and max flow. Lecture notes, slides, the eight homework handouts, section problems with solutions and the Python notebooks are public; recordings, homework solutions and exams are not.
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.