[WIP] Update README.md for recruiter-friendly presentation

[Copilot]

• Understand current README.md structure
• Add bilingual language switcher at the top of README.md
• Expand ## Highlights section with Dual Implementation Approach, LaTeX Reports mention, and Canny edge detection note
• Add ## Skills Demonstrated section between Highlights and Project Structure
• Add ## License section at the bottom of README.md
• Create README.vi.md with full Vietnamese translation matching the English structure

Original prompt

Goal

Upgrade the README.md of Bravee9/Computer-Vision-MAT3562 to be more professional and recruiter-friendly. The owner is preparing for job applications. The current README is already good but needs several improvements.

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Current README

Reference: https://github.com/Bravee9/Computer-Vision-MAT3562/blob/main/README.md

The current README is already well-structured with badges, sections, and author info. However it needs the following improvements:

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Issues to Fix

1. Add bilingual language switcher

Add at the very top (before the <div align="center"> block):

**English** | [Tiếng Việt](README.vi.md)

2. Add repo description link / topic tags note

Not in README, but add a Topics/Keywords section for discoverability.

3. Expand the ## Highlights section

The current Highlights section only lists from-scratch implementations and the Lab 03 mini-project pipeline. Add more depth to showcase skills to recruiters:

• Mention that all algorithms were implemented both from scratch (NumPy) AND with library (OpenCV) — with results cross-verified
• Mention the LaTeX reports that demonstrate theoretical understanding
• Add a note about the Canny edge detection pipeline

4. Add a ## Skills Demonstrated section (NEW)

This is important for job applications. Add a new section between ## Highlights and ## Project Structure:

## Skills Demonstrated

| Area | Details |
|------|---------|
| Image Processing | Histogram equalization, point transforms, spatial filtering |
| Frequency Domain | DFT/FFT, ideal & Gaussian LP/HP filters |
| Computer Vision | Canny edge detection, Otsu thresholding, morphological ops |
| Implementation | NumPy from-scratch verified against OpenCV — ensures deep algorithmic understanding |
| Documentation | LaTeX technical reports with theory derivations and experimental analysis |
| Tools | Python 3, OpenCV, NumPy, scikit-image, Matplotlib, Jupyter, LaTeX |

5. Fix author section — add email placeholder or note

Currently the author section only has GitHub and LinkedIn. This is fine, keep it as is but ensure the LinkedIn URL https://www.linkedin.com/in/brave9/ is correct (keep as-is).

6. Add a ## License section at the bottom

Add a simple license/usage note:

## License

Academic project — for educational reference only.  
© 2025–2026 Bùi Quang Chiến — Hanoi University of Science, VNU

7. Create README.vi.md

Create a Vietnamese version README.vi.md with translated content matching the structure of the English README. The Vietnamese content should include:

• Same header with badges but with Vietnamese subtitle: Đại học Khoa học Tự nhiên, ĐHQG Hà Nội | 2025–2026
• Language switcher at top: [English](README.md) | **Tiếng Việt**
• Translated Overview, Labs table, Highlights, Skills Demonstrated, Project Structure, Tech Stack, Quick Start, Author sections
• All section headings in Vietnamese
• Keep code blocks and technical terms in English

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Files to create/modify

• Modify: README.md — add Skills Demonstrated section, expand Highlights, add License section, add language switcher link at top
• Create: README.vi.md — full Vietnamese translation with same structure

The following is the prior conversation context from the user's chat exploration (may be truncated):

User: # Machine Learning

English | Tiếng Việt

Comprehensive implementation of fundamental machine learning algorithms from probability-based methods to deep neural networks and clustering techniques.

Overview

12 weeks of practical ML implementations. Course materials from VNU University of Science, Hanoi - Machine Learning (MAT 3533) - Academic Year 2025-2026.

Course Structure

Week	Topic	Algorithms	Key Dataset
1	Probability Classification	Gaussian Naive Bayes	Email Spam (4601 samples)
2	Binary Classification	Bernoulli Naive Bayes	Medical Diagnosis
3	Regression	Linear Regression	SAT-GPA Prediction
4	Classification Methods	Logistic Regression, KNN	Banking, Admission
5	Advanced Classifiers	SVM, Decision Trees	MNIST, Iris, Glass
6	Dimensionality Reduction	PCA	Parkinson's Speech (754 features)
7	Linear Discriminant	LDA	MNIST, Face Recognition
8	Neural Network Basics	Perceptron	Sonar (Rock vs Mine)
9	Deep Learning	Multi-Layer Perceptron	Dry Bean (7 classes)
10	Distance Clustering	K-Means, DBSCAN	MNIST, Synthetic Data
11	Probabilistic Clustering	Gaussian Mixture Model	Iris, Shopping Data
12	Hard Margin SVM	SVM with CVXOPT	Breast Cancer, Sonar

Installation

git clone https://github.com/Bravee9/Machine-Learning-MAT3533.git
cd Machine-Learning-MAT3533
pip install -r requirements.txt

Libraries:

Python 3.8+, NumPy, Pandas, Matplotlib, Scikit-learn, CVXOPT

Usage

cd week-XX-topic
jup...

</details>



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