Lesson 012: Bayesian Beta-Binomial Smoothing
The Artemis vote system shows 50 random images per ballot and asks voters to pick 5 favorites. With 500 ballots across 1...
algorithms (19 lessons)
Lesson 013: Elo Rating for Image Comparison
The Artemis pairwise voting mode shows two images side by side and asks "which is better?" This produces binary outcomes...
Lesson 014: Bradley-Terry-Luce and When to Skip It
We have pairwise comparison data (image A beats image B) and want the best possible strength estimates. Bradley-Terry-Lu...
Lesson 015: Borda Count for Ranked Voting
The Artemis category voting mode asks voters to rank their top 3 images within a category. We need to convert these part...
Lesson 016: Krippendorff's Alpha for Sparse Agreement
We want to measure whether voters agree on which images are good. With 100 voters and 12,217 images, the voter-image mat...
Lesson 017: Composite Scoring with Heterogeneous Signals
We have three different types of preference data — batch selection rates, Elo ratings from pairwise comparisons, and Bor...
Lesson 018: Run-ID Partitioned Scoring
The scoring pipeline will be re-run as new vote data arrives, as scoring methods are tuned, or as bugs are fixed. Each r...
Lesson 022: Heuristic Month-Fit Scoring Without Text Metadata
When images lack text metadata (titles, descriptions, captions), month or season suitability can still be approximated f...
Lesson 023: Maximum Marginal Relevance for Diversity-Aware Selection
Greedy Maximum Marginal Relevance (MMR) is the practical default for selecting a diverse, high-quality subset from a lar...
Lesson 024: Hungarian Algorithm for Optimal Assignment
When you need to assign N items to N slots where each item-slot pair has a fitness score, the Hungarian algorithm gives ...
Lesson 028: Chi-Squared Tests for Bias Detection at Small Scale
We planted known biases in synthetic vote data — 10% of voters had position bias (preferring earlier-displayed images), ...
Lesson 032: Startup Cache for Interactive Scoring
When an interactive web app needs sub-100ms responses from a scoring function that depends on large lookup tables, load ...
Lesson 045: Embedding-Based Deduplication for Image Collection Curation
When working with a large image collection from an automated source, assume near-duplicates dominate the pool until prov...
Lesson 048: Greedy Max-Min Diversity Selection
To select k items that maximally represent the diversity within a group, iteratively pick the item most distant from all...
Lesson 050: Connected Components for Transitive Deduplication
When deduplicating by pairwise similarity, use graph connected components to group items — not naive pair-based merging....
Lesson 051: Sigmoid Calibration for Domain-Specific CLIP Scores
CLIP logits have domain-specific distributions. Converting them to meaningful [0,1] confidence scores requires a sigmoid...
Lesson: Algorithm Selection — sklearn KMeans vs Pillow Quantize for Dominant Colors
The visual feature extraction pipeline ran sklearn's KMeans on every thumbnail to find 5 dominant colors. Each call took...
Lesson: Choosing k for k-Means Clustering in a Constrained Selection Problem
We needed to cluster 12,217 Artemis II mission photos into visually distinct groups. The clusters serve a specific downs...
Choosing the Right Similarity Algorithm
Before choosing a similarity algorithm, understand whether your data uses binary membership (item has feature or doesn't...