Overview of Laboratory Exercises in ML EduLab

The laboratory exercises within the ML EduLab platform are designed to provide students with an intuitive, hands-on approach to learning core concepts in machine learning. Each lab follows a structured, interactive workflow that allows users to experiment with algorithms and instantly observe the results of their configurations.

🧩 Common Characteristics

• Web-Based Interface: All labs are accessible directly through a browser—no installation or programming required.
• Interactive Design: Users can configure model parameters through simple UI elements such as sliders, input fields, and selection menus.
• Visual Feedback: Each lab includes real-time graphs and visualizations that show training progress, error metrics, model structure, and prediction outcomes.
• Consistency: All exercises follow a similar layout and user experience, making it easier for students to transition from one concept to another.

🎯 Purpose

• To support academic instruction in machine learning through structured, interactive practice.
• To help students develop an intuitive understanding of model behavior, parameter tuning, and performance evaluation.
• To assist educators in creating reproducible and guided lab environments that align with course objectives.

⚙️ How It Works

• Dataset Loading: Students begin by uploading or selecting a dataset suitable for the task.
• Model Configuration: Parameters such as learning rate, number of epochs, network structure, or kernel type (depending on the lab) are set using the graphical interface.
• Training and Testing: The model is trained and validated using the selected parameters. Visual outputs provide insight into the learning process.
• Evaluation and Interpretation: Error charts, decision boundaries, or recognition results help students analyze model performance.