# fall25_algods3

**Repository Path**: seiiib/fall25_algods3

## Basic Information

- **Project Name**: fall25_algods3
- **Description**: Repository for the Algorithm & Data Structure 3 Class
- **Primary Language**: Python
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 5
- **Forks**: 0
- **Created**: 2025-10-25
- **Last Updated**: 2026-01-09

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Algorithm and Data Structure 3 - Introduction to Machine Learning

## Course Overview

This course provides a comprehensive introduction to Machine Learning, covering fundamental algorithms and techniques for both supervised and unsupervised learning. Students will gain hands-on experience implementing and applying machine learning models to solve real-world problems.

## Prerequisites

Before enrolling in this course, students must have completed:
- Algorithm and Data Structure 1 & 2
- Python Object-Oriented Programming (OOP)
- Linear Algebra

## Grading System

Your final grade will be calculated as follows:

| Work | Test type | Delivered | Percentage |
| --- | --- | --- | --- |
| Continuous Learning | MCQ | Individual | 40% |
| Homeworks| Labs | Individual | 30% |
| Final Exam | Exam Paper OR Project | Individual | 30% |

**Total**: 100%

### Bonus and Malus Points

| Category | Description | Points |
| --- | --- | --- |
| 1. Speaking out loud ***bonus*** | For those who participate in class, and share their knowledge to their comrades, keep continue | +0.5 |
| 2. No dumb question ***bonus*** | For those who do not hesitate to ask questions, even with a translator app in hand, keep continue | +0.5 |
| 3. Always here ***bonus*** | For those who attend EVERY class, no matter the circumstances and stomachache, keep continue | +0.5 |
| 4. Never here ***malus*** | For those who are never here, without justifying why | -0.5/3 abs |
| 4. Video Games ***malus*** | For those who play video games during the class session | -0.5 every time caught playing |

## Repository Organization

This repository is structured as follows:

```
. 
fall25_algods3/
    README.md
    labs/           # Lab assignments and exercises
        week_1/
    lessons/        # Course slides and lecture materials
        week_1/
```

- **`/labs`**: Contains all lab assignments and practical exercises
- **`/lessons`**: Contains all course slides and lecture materials

### Git Workflow for Students

Students are expected to follow this Git workflow:

1. **Create your own branch**: Create a personal branch with your name (e.g., `student id + english name`)
   ```bash
   git checkout -b '2023900900_seb'
   ```

2. **Work on assignments**: Complete your lab work and projects on your branch

3. **Commit regularly**: Make meaningful commits as you progress
   ```bash
   git add .
   git commit -m "Descriptive message about your work"
   ```

4. **Push before deadlines**: Push your completed work to your branch before each deadline
   ```bash
   git push origin '2023900900_seb'
   ```

**Important**: Always push your finished work by the expected deadline. Late submissions may not be accepted.

## Required Tools and Setup

Students must install and configure the following tools:
- **VSCode** - Primary development environment
- **Jupyter Notebooks** - For interactive coding and documentation
- **Python packages**: scikit-learn, pandas, numpy, matplotlib, and other ML libraries

Detailed installation instructions will be provided in Week 1.

## Learning Objectives

By the end of this course, students will be able to:
- Implement and apply fundamental machine learning algorithms
- Understand the theory behind supervised and unsupervised learning
- Evaluate and optimize model performance
- Work on real-world ML projects
- Select appropriate algorithms for different problem types
- Interpret and communicate machine learning results effectively

## Course Schedule

### Week 1: Introduction and Installation
- Course overview and expectations
- Machine learning fundamentals and concepts
- Setting up the development environment (VSCode, Jupyter, Python packages)
- Introduction to the ML workflow

### Week 2: Linear Regression, Logistic Regression and Gradient Descent
- Linear regression theory and implementation
- Logistic regression for classification
- Gradient descent optimization
- Cost functions and model evaluation

### Week 3: Decision Trees and Random Forest
- Decision tree algorithms
- Tree pruning and hyperparameters
- Random forest ensemble method
- Feature importance and interpretation

### Week 4: K-Means Hierarchical Clustering
- Introduction to unsupervised learning
- K-Means clustering algorithm
- Hierarchical clustering methods
- Clustering evaluation metrics

### Week 5: Regularization
- Overfitting and underfitting
- L1 (Lasso) and L2 (Ridge) regularization
- Elastic Net
- Cross-validation techniques

### Week 6: Ensemble Methods
- Bagging and boosting concepts
- AdaBoost algorithm
- Gradient Boosting
- Stacking and blending techniques

### Week 7: Ensemble Methods (Continued)
- Advanced ensemble techniques
- XGBoost and LightGBM
- Model optimization and tuning
- Practical applications and case studies

### Week 8: Final Exam Project
- Individual project presentations
- Final exam
- Course wrap-up and next steps

## Lab Work

Labs will be conducted throughout the course to reinforce theoretical concepts with practical implementation. Students are expected to:
- Complete lab assignments on time
- Document their code and findings
- Participate actively in lab sessions
- Apply best practices in code organization and documentation

## Final Project

... Incoming

## Additional Resources

- Course materials and lecture notes will be provided weekly
- Recommended readings and tutorials will be shared throughout the course
- Office hours available for additional support