Chapter 01: ML Basics

This chapter introduces the basic concepts of Machine Learning. We focus on supervised learning, explain the difference between regression and classification, show how to evaluate and compare Machine Learning models and formalize the concept of learning.

Chapter 1.1: What is ML?

As subtopic of artificial intelligence, machine learning is a mathematically well-defined discipline and usually constructs predictive or decision models from data, instead of explicitly programming them. In this section, you will see some typical examples of where machine learning is applied and the main directions of machine learning.

Chapter 1.2: Data

In this section we explain the basic structure of tabular data used in machine learning. We will differentiate targets from features, talk about labeled and unlabeled data and introduce the concept of the data generating process.

Chapter 1.3: Tasks

The tasks of supervised learning can roughly be divided in two categories: regression (for continuous outcome) and classification (for categorical outcome). We will present some examples.

Chapter 1.4: Models and Parameters

This section introduces the concept of models. We will explain that a model maps features to predictions, and we will show that models can be expressed by parameters.

Chapter 1.5: Learner

In this section we introduce the concept of a learner: Roughly speaking, it takes a training set and gives back a model.

Chapter 1.6: Losses and Risk Minimization

In order to find "good" ML models, we need a concept to evaluate and compare models. To this end, the concepts of "loss function", "empirical risk" and "empirical risk minimization" are introduced.

Chapter 1.7: Optimization

In this section we introduce optimization which is necessary to perform the former introduced concept of risk minimization computationally efficient.

Chapter 1.8: Components of a Learner

Nearly all supervised learning algorithms can be described in terms of three components: 1) hypothesis space, 2) risk, and 3) optimization. In this section, we explain how these components work together and why this is a very useful concept for many supervised learning approaches.