# RedstoneWill/HsuanTienLin_MachineLearning

Repository: https://github.com/RedstoneWill/HsuanTienLin_MachineLearning
Canonical: https://ross.abutalabs.com/products/hsuantienlin_machinelearning
License Family: other
Topics: machine-learning
Last push: 2020-03-01T07:04:56+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3042, "days_push": 2376, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2775, forks 840 (observed 2026-08-28T04:07:21.180298+00:00)

## What it is
A curated collection of notes, videos, and study materials for Hsuan-Tien Lin's NTU machine learning courses 'Machine Learning Foundations' and 'Machine Learning Techniques'. It organizes all 32 lectures with links to blog write-ups covering theory and classic algorithms.

## Use cases
- learn machine learning fundamentals from a structured course
- study SVM, decision trees, and boosting algorithms
- find notes for the Hsuan-Tien Lin Coursera courses
- understand VC dimension and learning theory
- prepare for machine learning interviews with classic algorithms

## When to choose
- you want a free, well-organized companion to the NTU machine learning lectures
- you prefer Chinese-language notes and explanations
- you need a structured path from ML theory to classic algorithms

## When to avoid
- you need a software library or runnable code
- you want modern deep learning content only
- you need English-language materials exclusively

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning
- domain: machine-learning, tutorials, education
- platform: cross-platform
- tags: course-notes, hsuan-tien-lin, ntu, chinese-language, study-materials

## Member repositories
- RedstoneWill/HsuanTienLin_MachineLearning (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:21.180298+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T08:16:33.294324+00:00, confidence not recorded.
  - readme: https://github.com/RedstoneWill/HsuanTienLin_MachineLearning (fetched 2026-08-28T04:07:21.180298+00:00, sha 94e33009c98b)
- Data as of 2026-08-30T08:39:29.467469+00:00.