Lectures 13–18 and Project 4 move from factor operations and variable elimination to exact inference and particle filtering, letting Pacman track invisible ghosts through noisy distance sensors.
Lectures 26–28 close with nuclear monitoring, AI safety, and reflection. Independent completion should preserve assumptions, test evidence, and failure analysis for Projects 1–5 instead of reporting only autograder scores.
Lectures 5–8 use CSPs to practice variables, constraints, and search order before Project 2 implements minimax, alpha-beta, and expectimax. Their key difference is the assumption made about other agents.
Lectures 19–25 connect rational decisions and VPI to machine learning, while Project 5 uses PyTorch for regression, classification, CNNs, attention, and an optional character-GPT.
Lectures 9–12 and Project 3 use the same Gridworld to contrast value iteration with a known model, Q-learning from unknown dynamics, and approximate Q-learning that generalizes through features.
Lectures 1–4 and Project 1 connect DFS, BFS, UCS, A*, state representation, and heuristic design. The goal is not memorizing algorithms but separating what the frontier, cost, and state each control.
CS188 Spring 2026 publishes 28 recordings, 27 lecture slide sets, 11 discussions, and Projects P0–P5. P0 is a Python/autograder tutorial, P1–P4 use Pacman settings, and P5 contains general machine-learning tasks.
Spring 2025 at people.eecs.berkeley.edu/~jrs/189s25 is the only A3 self-study edition with notes, videos, HW1–7, code/data and past exams; Fall 2026 at eecs189.org/fa26 has a 27-lecture schedule but most materials are not yet released and the rotating site can 404 old URLs.
The final seven lectures move from exploration and theoretical limits through two review lectures to advanced exploration, multitask RL, and unresolved research problems.
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.
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.
L11–18 connect control as inference, LLM RL, model-based RL, and offline RL, with HW4 and HW5 providing two compute-intensive implementations.
L5–10 build the deep-RL core through policy- and value-based routes; HW2 is CPU-friendly, while HW3's Atari and HalfCheetah runs can require hours of GPU time.
Spring 2026 CS185/285 publishes slides for 25 lectures, nine discussion units, five assignments, and starter code; current recordings require bCourses access, while HW4 defaults to an H100, so this is not a zero-cost open course.
Units 15–18 place NLP models inside perception, reasoning, tool, and environment loops; the question shifts from next-token prediction to allocating inference compute and validating multi-step action.
The first four units make text countable, representable, and classifiable; A1 then moves from n-grams and perceptrons to an NBOW MLP.
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.
Units 08–12 turn a base model into an interactive system: pre-training establishes capability, post-training shapes behavior, and generation plus evaluation determine how outputs are used.
Units 13–14 connect models to external knowledge; A3 requires data collection, QA annotation, indexing, and ablations under CPU and latency constraints.
Units 05–07 move from recurrent state to encoder-decoder models, then rewrite the information path with attention and Transformer blocks.
CMU 15-281's Search and Games explicitly credits Berkeley's Pacman AI projects. The official course site separately lists a zero-point P0 tutorial and five programming assignments, P1–P5.
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.