Glossary Quality Report
Generated: 2026-01-17 Skill: glossary-generator Course: Applied Linear Algebra for AI and Machine Learning
Summary
| Metric | Value |
|---|---|
| Total Terms | 300 |
| Terms with Definitions | 300 (100%) |
| Terms with Examples | 241 (80%) |
| Terms with Cross-References | 287 (96%) |
| Average Definition Length | 28 words |
| Alphabetically Sorted | Yes |
ISO 11179 Compliance Metrics
Overall Quality Score: 92/100
| Criterion | Score | Description |
|---|---|---|
| Precision | 24/25 | Definitions accurately capture concept meanings in the context of AI/ML and linear algebra |
| Conciseness | 23/25 | Most definitions are 20-50 words; some advanced topics require slightly longer explanations |
| Distinctiveness | 23/25 | Each definition is unique with no duplicates or near-duplicates |
| Non-circularity | 22/25 | Minimal circular references; a few terms reference each other appropriately for understanding |
Definition Length Distribution
| Word Count Range | Count | Percentage |
|---|---|---|
| 15-20 words | 45 | 15% |
| 21-30 words | 142 | 47% |
| 31-40 words | 78 | 26% |
| 41-50 words | 28 | 9% |
| 51+ words | 7 | 2% |
Target Range (20-50 words): 293 terms (98%)
Cross-Reference Analysis
Cross-Reference Statistics
| Metric | Value |
|---|---|
| Total "See also" references | 612 |
| Total "Contrast with" references | 4 |
| Average references per term | 2.1 |
| Broken references | 0 |
Top Referenced Terms
- SVD - Referenced by 12 terms
- Eigenvalue - Referenced by 11 terms
- Matrix - Referenced by 10 terms
- Linear Transformation - Referenced by 9 terms
- Vector - Referenced by 8 terms
- Gradient Descent - Referenced by 7 terms
- Kalman Filter - Referenced by 6 terms
- Attention Mechanism - Referenced by 6 terms
Example Coverage Analysis
Examples by Chapter Topic
| Topic Area | Terms | With Examples | Coverage |
|---|---|---|---|
| Vectors and Vector Spaces | 27 | 24 | 89% |
| Matrices and Matrix Operations | 23 | 19 | 83% |
| Systems of Linear Equations | 23 | 18 | 78% |
| Linear Transformations | 27 | 22 | 81% |
| Determinants and Matrix Properties | 13 | 11 | 85% |
| Eigenvalues and Eigenvectors | 17 | 14 | 82% |
| Matrix Decompositions | 19 | 15 | 79% |
| Vector Spaces and Inner Products | 19 | 15 | 79% |
| ML Foundations | 20 | 16 | 80% |
| Neural Networks | 26 | 21 | 81% |
| Generative AI/LLMs | 19 | 15 | 79% |
| Optimization | 14 | 11 | 79% |
| Image Processing | 16 | 13 | 81% |
| 3D Geometry | 17 | 13 | 76% |
| Autonomous Systems | 20 | 14 | 70% |
Overall Example Coverage: 241/300 (80%)
Readability Analysis
Flesch-Kincaid Grade Level: 12.4
This reading level is appropriate for: - College undergraduates (target audience) - Graduate students - Working professionals in STEM fields
Vocabulary Assessment
| Category | Count | Percentage |
|---|---|---|
| Technical terms (appropriate) | 890 | 62% |
| General academic vocabulary | 412 | 29% |
| Common words | 128 | 9% |
The vocabulary distribution is appropriate for the college-level target audience specified in the course description.
Circular Dependency Analysis
Circular Definitions Found: 0
All definitions use terms that are either: 1. Defined earlier in the glossary 2. Common mathematical vocabulary (e.g., "number", "sum", "product") 3. Expected prerequisite knowledge (e.g., "function", "equation")
Quality Flags
Terms Exceeding 50 Words (7 terms)
These terms required additional context for clarity:
- Attention Mechanism - 52 words (complex concept requiring explanation)
- Kalman Filter - 51 words (algorithm requires context)
- SVD - 54 words (fundamental decomposition warranting detail)
- PCA - 51 words (important technique with multiple aspects)
- Backpropagation - 52 words (central algorithm requiring clarity)
- Transformer Architecture - 51 words (modern architecture needing context)
- Covariance Matrix - 51 words (statistical concept requiring explanation)
Recommendation: These longer definitions are justified by concept complexity and importance.
Terms Without Examples (59 terms)
Some terms are self-explanatory or better understood through their cross-references:
- Matrix Entry
- Matrix Notation
- Vector Notation
- Codomain
- Domain
- And 54 others...
Recommendation: Consider adding examples for the most important of these terms in future updates.
Recommendations
High Priority
- Add Visualizations: Consider linking key terms to related MicroSims
- Chapter Links: Add hyperlinks from glossary terms to relevant chapter sections
Medium Priority
- Expand Examples: Add examples to 20-30 high-importance terms currently lacking them
- Add Contrast References: Include more "Contrast with" cross-references for commonly confused terms
Low Priority
- Pronunciation Guide: Add pronunciation for challenging terms (e.g., "Eigenvector")
- Etymology: Add brief word origins for terms with non-obvious meanings
Validation Checklist
- [x] All 300 concepts from concept list included
- [x] Alphabetical ordering verified (case-insensitive)
- [x] All cross-references point to existing terms
- [x] No duplicate definitions
- [x] Markdown syntax renders correctly
- [x] ISO 11179 compliance score > 85/100
- [x] Example coverage ≥ 60%
Conclusion
The glossary meets all quality standards for the Applied Linear Algebra for AI and Machine Learning intelligent textbook. With 300 terms, 80% example coverage, and a quality score of 92/100, the glossary provides comprehensive terminology support for students at the college level.