01
AI Foundations
Understand what AI is, how systems learn, where they fail, and how to judge claims without hype.
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Five outcome-based courses
Each course includes explicit outcomes, mapped competencies, practice activities, and an applied capstone.
02
Responsible AI User
Use AI productively while protecting privacy, checking outputs, and preserving human accountability.
03
AI at Work
Evaluate workplace use cases, run safe pilots, measure value, and communicate changes responsibly.
04
AI Policy & Society
Analyze AI systems through rights, equity, governance, safety, and public-interest outcomes.
05
Building with AI Systems
Understand language models, retrieval, agents, evaluation, cost, and deployment safeguards through practical system design.
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FundamentalsCore concepts: how AI works at a high level.84 guides Language AILLMs, NLP, ChatGPT, prompting, and text understanding.155 guides Visual AIImage generation, computer vision, video, and design.133 guides Audio AIVoice, music, podcasting, and audio-first AI.117 guides CompaniesProfiles of OpenAI, Anthropic, Google, Meta, and more.121 guides ApplicationsHow AI shows up in coding, search, agents, and daily life.111 guides IndustriesAI in healthcare, finance, education, energy, and more.91 guides TechnicalChips, fine-tuning, RAG, vectors, and AI infrastructure.177 guides SocietyBias, privacy, copyright, safety, and the AI future.30 guides
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Language AI
Minimum Bayes Risk Decoding
Minimum Bayes Risk (MBR) decoding picks the output that is most similar to many other likely outputs, rather than the single highest-probability one.
Language AITypical Sampling
Typical sampling is a text-generation method that picks the next word from tokens whose information content sits close to the model's expected surprise…
Language AIMirostat Perplexity Control
Mirostat is a decoding algorithm that actively steers a language model's output toward a target perplexity (a set level of surprise) using a feedback loop.
Language AISycophancy in Language Models
Sycophancy is the tendency of AI language models to tell users what they want to hear, agreeing with stated opinions or caving to pushback even…
Language AISpeculative Sampling Verification
Speculative sampling speeds up large language model generation by letting a small 'draft' model guess several tokens ahead, then having the big model verify…
Language AIEntropy-Based Sampling
Entropy-based sampling adapts how an LLM picks its next token based on how uncertain the model is at that moment.
Language AIELECTRA Pretraining
ELECTRA is a more efficient way to pretrain language models by teaching them to spot fake words instead of guessing hidden ones.
Language AIRoBERTa Training Recipe
RoBERTa showed that BERT was significantly undertrained: by tuning the recipe rather than the architecture, it set new benchmark records.
Language AIXLNet Permutation Modeling
XLNet blends the bidirectional context of BERT with the autoregressive prediction of GPT by training over random word orderings.
Language AITree of Thoughts
Tree of Thoughts (ToT) is a prompting framework that lets a language model explore multiple reasoning paths in parallel, like branches of a tree, instead…
Language AISelf-Consistency Decoding
Self-consistency is a decoding strategy that samples many different reasoning paths from a language model and then picks the answer most of them agree on.
Language AIOdds Ratio Preference Optimization
Odds Ratio Preference Optimization (ORPO) is a fine-tuning method that teaches a language model good behavior and human preferences in a single training pass.
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