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Higher educationMaster’s

Master’s · Year 1

Machine learning fundamentals: regression, classification and neural networks.

  • 3 topics
  • 2 chapters
  • 8 min
  • 1 textbook
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3 topics

  1. 1

    Supervised and Unsupervised Learning

    Types of machine learning: classification and regression on labeled data, clustering on unlabeled data. Learn to evaluate a model on a test set and to fight overfitting.

    Chapter 1. Machine learning fundamentals2 min

  2. 2

    Linear Regression and Gradient Descent

    We examine the linear regression model, mean squared error and least squares. You will learn to find parameters with gradient descent and evaluate results with RMSE and R2.

    Chapter 1. Machine learning fundamentals3 min

  3. 3

    Perceptron and Multilayer Networks

    The artificial neuron, Rosenblatt’s perceptron and its limit, the XOR problem. You will learn how hidden layers, activation functions and backpropagation make it possible to train a multilayer network.

    Chapter 2. Neural networks3 min

Master’s · Year 1: textbooks

Textbooks and study guides for this level.