kr7001/IT1244-teaching-materials

Tutorial materials for NUS IT1244: Artificial Intelligence - Technology and Impact (AY25/26 Sem 2)

What's novel

Tutorial materials for NUS IT1244: Artificial Intelligence - Technology and Impact (AY25/26 Sem 2)

Code Analysis

4 files read · 2 rounds

A collection of small, pedagogical Python scripts and Excel spreadsheets that demonstrate fundamental AI algorithms (binary search tree traversal, 3-opt TSP heuristic, gradient descent, closed-form linear regression) for a university introductory AI course.

Strengths

The code is correct, well-commented, and appropriately scoped for teaching — each script isolates one algorithm and makes the math transparent. The 3-opt Hamiltonian path implementation is a genuine (if small) local search heuristic, not just a wrapper.

Weaknesses

No error handling, no input validation, no tests, and no separation between algorithm logic and hardcoded data. The Excel-based tutorials (KNN, logistic regression, K-means) offer no executable code at all, limiting reproducibility.

Score Breakdown

Innovation

3 (25%)

Craft

41 (35%)

Traction

14 (15%)

Scope

47 (25%)

Signal breakdown

Innovation

Not Fork+1

Code Novelty+0

Concept Novelty+0

Craft

Ci+0

Tests-1

Polish+0

Releases+0

Has License+0

Code Quality+12

Readme Quality+15

Recent Activity+7

Structure Quality+4

Commit Consistency+4

Has Dependency Mgmt+0

Traction

Forks+0

Stars+6

Hn Points+0

Watchers+3

Early Traction+5

Devto Reactions+0

Community Contribs+0

Scope

Commits+5

Languages+3

Subsystems+0

Bloat Penalty+0

Completeness+5

Contributors+5

Authored Files+8

Readme Code Match+3

Architecture Depth+3

Implementation Depth+8

Evidence

Commits

Contributors

Files

Active weeks

TestsCI/CDREADMELicenseContributing

Repository

Language

Python

Stars

Forks

License

—