SML Course Exercises

This repository contains exercise solutions for the Statistical Machine Learning course at UPB.

📂 Practice Exercises

1. Linear Discriminant Analysis (LDA)

• Implementation of LDA classifier
• Data visualization and analysis
• Location: Exercise/Practice 1 Linear Discriminant Analysis/

2. Least Squares SGD

• Implementation of Stochastic Gradient Descent
• Linear regression with least squares
• Location: Exercise/Practice 2 Least squares SGD/

3. Rosenblatt Perceptron and SVM

• Perceptron algorithm implementation
• Support Vector Machine basics
• Location: Exercise/Practice 3 Rosenblatt perceptron and SVM/

4. Algorithmic Differentiation - Reverse Mode

• Implementation of reverse mode automatic differentiation
• Gradient computation
• Location: Exercise/Practice 4 Algorithmic Differentiation - Reverse Mode/

5. Mixture Models

• Implementation of Gaussian Mixture Models
• Expectation-Maximization algorithm
• Location: Exercise/Practice 5 Mixture Model/

🛠 Setup

Prerequisites

• Python 3.x
• Required Python packages (install via pip install -r requirements.txt)

Directory Structure

Exercise/
├── Practice 1 Linear Discriminant Analysis/
├── Practice 2 Least squares SGD/
├── Practice 3 Rosenblatt perceptron and SVM/
├── Practice 4 Algorithmic Differentiation - Reverse Mode/
└── Practice 5 Mixture Model/

📝 Usage

1. Navigate to the specific exercise directory
2. Follow the instructions in the exercise PDF
3. Implement your solution in the provided Python templates
4. Test your implementation

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Last updated: August 2025