COURSE 23Intermediate9 IN-DEPTH LESSONS

Research Literacy and Self-Study

Empowering independent research: how to dissect arXiv preprints, audit claims against experimental evidence, understand ablations, and spot reproducibility gaps.

Start Lesson 1: How to read an AI paper →← All 29 Courses

Course Syllabus & Units

Unit 1

Overview

1 lessons
1.1L1

How to read an AI paper

A practical method to read AI research without drowning: claims, evidence, limitations, and what to ignore on the first pass. NeurIPS paper checklist guidance explicitly asks whether abstract/intro claims match the paper’s contributions ...

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Unit 2

Skepticism

1 lessons
2.1L1

Demo skepticism

A habit of questioning polished AI demos: selection effects, hidden humans, cherry-picked prompts, unreproducible conditions, and evals that do not measure the claimed construct. Ties to research literacy (papers) and measurement literac...

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Unit 3

Plan

1 lessons
3.1L1

Building a free study plan

How to assemble a free path that rivals paid certificate browsing: ANN Learn paths + Elements of AI / fast.ai / open course pages / papers / measurement & governance syllabi—with **projects as proof**. <!-- IMAGE: weekly calendar — chapt...

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Unit 4

Repro

1 lessons
4.1L1

Reproducibility checklist

A practical checklist for making ML/AI results redoable: data versions, code, seeds, hardware notes, eval scripts, and honest nondeterminism. It aligns with the NeurIPS paper checklist’s reproducibility / code-data / experimental-detail ...

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Unit 5

Arxiv

1 lessons
5.1L1

arXiv hygiene

How to use **arXiv** wisely: preprints ≠ peer-reviewed gospel; versioning; finding code; spotting overclaims; preferring proceedings/camera-ready when stakes are high. <!-- IMAGE: arXiv abstract page with v1/v2 tabs highlighted -->

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Unit 6

Checklist

1 lessons
6.1L1

NeurIPS paper checklist literacy

The **NeurIPS Paper Checklist** is a required questionnaire bundled with NeurIPS submissions to encourage responsible ML research practices: reproducibility, transparency, ethics, and societal impact. Official guidance states papers with...

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Unit 7

Reproducibility

1 lessons
7.1L1

ML reproducibility practices

Practices that make ML results trustworthy enough for others to verify: shared code/data when possible, precise methods, honest statistics, and a culture that rewards careful reproduction—including partial and negative results. Anchors: ...

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Unit 8

Datasheets

1 lessons
8.1L1

Datasheets for datasets

**Datasheets for Datasets** (Gebru et al.) proposes that every dataset ship with a structured document—like electronics datasheets—covering motivation, composition, collection, preprocessing, recommended uses, distribution, and maintenan...

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Unit 9

Measurement

1 lessons
9.1L1

AI measurement science intro

**AI measurement science** asks whether evaluation scores actually measure the abilities we claim—and how to design, analyze, and govern benchmarks more rigorously. Stanford **CS321M** (Spring 2026) teaches this as a graduate course; the...

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