ML Foundations

Level: Beginner Duration: 60 minutes Download PDF

Machine Learning Foundations

The starting point for understanding how machines learn from data.

Learning Outcomes

By completing this topic, you will:

  • Understand the three main types of machine learning (supervised, unsupervised, reinforcement)
  • Explain the difference between training and inference
  • Identify overfitting and underfitting in model performance
  • Apply the train-test split methodology

Visual Guides

Learning Paradigms
Learning Paradigms
ML vs Traditional Programming
ML vs Traditional Programming
Performance Scaling
Performance Scaling

Prerequisites

No prior ML knowledge required. Basic familiarity with:

  • Data concepts (rows, columns, features)
  • Basic statistics (mean, variance)

Key Concepts

Types of Learning

  • Supervised Learning: Learn from labeled examples (input-output pairs)
  • Unsupervised Learning: Discover patterns without labels
  • Reinforcement Learning: Learn through trial and reward

The Learning Process

  1. Collect and prepare data
  2. Choose a model architecture
  3. Train on training data
  4. Evaluate on test data
  5. Deploy and monitor

Bias-Variance Tradeoff

Balance between:

  • High bias (underfitting): Model too simple, misses patterns
  • High variance (overfitting): Model too complex, memorizes noise

When to Use

Machine learning is appropriate when:

  • You have sufficient historical data
  • Patterns exist but are hard to code manually
  • Predictions or classifications add value
  • The problem is well-defined

Common Pitfalls

  • Training and testing on the same data (data leakage)
  • Ignoring class imbalance in classification
  • Not normalizing features with different scales
  • Overfitting to small datasets

(c) Joerg Osterrieder 2025