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learningguideReading Berkeley CS189 Introduction to Machine Learning

Spring 2025 Overview: HW1–7 with Code and Data You Can Run, Plus What Fall 2026 Looks Like | Berkeley CS189 Course Overview

CS189 exists online in several terms. From post 2 onward this series uses Spring 2026 (eecs189.org/sp26, A3) as its base, walking through each lecture block and assignment; Spring 2025 (Shewchuk) is a different classic route with 25 lectures of notes, HW1–7 and past exams, but its official recordings sit behind a bCourses login; Fall 2026 is still in progress and serves only as a reference. This post is the series entry point and full table of contents.

#berkeley#cs189#machine-learning#open-course#learning-path
learningguideReading Berkeley CS285 Deep Reinforcement Learning

Exploration, RL Theory, Multitask Learning, and Open Problems | Berkeley CS285 Lectures 19–25

The final seven lectures move from exploration and theoretical limits through two review lectures to advanced exploration, multitask RL, and unresolved research problems.

#cs285#berkeley#exploration#reinforcement-learning#multi-task-learning
learningguideReading Berkeley CS285 Deep Reinforcement Learning

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.

#cs285#berkeley#deep-reinforcement-learning#gpu#self-study
learningguideReading Berkeley CS285 Deep Reinforcement Learning

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.

#cs285#berkeley#imitation-learning#reinforcement-learning#self-study
learningguideReading Berkeley CS285 Deep Reinforcement Learning

From Variational Inference and LLM RL to Offline RL | Berkeley CS285 Lectures 11–18

L11–18 connect control as inference, LLM RL, model-based RL, and offline RL, with HW4 and HW5 providing two compute-intensive implementations.

#cs285#berkeley#variational-inference#llm-rl#offline-rl
learningguideReading Berkeley CS285 Deep Reinforcement Learning

Policy Gradients, Actor-Critic, DQN, and SAC | Berkeley CS285 Lectures 5–10

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.

#cs285#berkeley#policy-gradient#q-learning#actor-critic
learningguideReading Berkeley CS285 Deep Reinforcement Learning

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.

#cs285#berkeley#deep-reinforcement-learning#self-study#ai-course
learningguideReading Berkeley CS288 Advanced Natural Language Processing

Inference-time Compute, Reasoning, and Embodied Agents | Berkeley CS288 Topic Guide

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.

#berkeley#cs288#ai-agent#reasoning#multimodal-ai#llm
learningguideReading Berkeley CS288 Advanced Natural Language Processing

From N-grams and Word Representations to Text Classification | Berkeley CS288 Topic Guide

The first four units make text countable, representable, and classifiable; A1 then moves from n-grams and perceptrons to an NBOW MLP.

#berkeley#cs288#nlp#language-model#text-classification
learningguideReading Berkeley CS288 Advanced Natural Language Processing

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.

#berkeley#cs288#nlp#llm#self-study#open-course
learningguideReading Berkeley CS288 Advanced Natural Language Processing

Pre-training, Post-training, Generation, and Evaluation | Berkeley CS288 Topic Guide

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.

#berkeley#cs288#llm#post-training#evaluation#prompting
learningguideReading Berkeley CS288 Advanced Natural Language Processing

Turning Retrieval, RAG, and Advanced Architectures into a System | Berkeley CS288 Topic Guide

Units 13–14 connect models to external knowledge; A3 requires data collection, QA annotation, indexing, and ablations under CPU and latency constraints.

#berkeley#cs288#rag#retrieval#llm#evaluation
learningguideReading Berkeley CS288 Advanced Natural Language Processing

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.

#berkeley#cs288#nlp#transformer#sequence-model
learningguideReading CMU 07-380 AI & ML II

Advanced 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.

#cmu#07-380#ai-course#machine-learning#learning-path
learningguideReading CMU 10-301 Introduction to Machine Learning

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.

#cmu#machine-learning#python#linear-algebra
learningguideReading CMU 10-301 Introduction to Machine Learning

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.

#cmu#machine-learning#decision-tree#information-theory
learningguideReading CMU 10-301 Introduction to Machine Learning

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.

#cmu#machine-learning#knn#perceptron#linear-regression
learningguideReading CMU 10-301 Introduction to Machine Learning

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.

#cmu#machine-learning#logistic-regression#optimization
learningguideReading CMU 10-301 Introduction to Machine Learning

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.

#cmu#machine-learning#neural-network#backpropagation
learningguideReading CMU 10-301 Introduction to Machine Learning

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.

#cmu#machine-learning#learning-theory#ai-ethics