RO47002 Machine learning for robotics

This course provides a broad overview of machine learning techniques and their practical application in robotics.

Intro to Machine Learning and python

  • Machine Learning in Robotics
  • Understanding the goal of machine learning, fundamental problems, high-level overview. Visualizing your data
  • Supervised vs Unsupervised vs Reinforcement learning
  • Model-driven vs Data-driven: White box, Gray box, Black box
  • Prior knowledge vs Unstructured. Feature extraction, linear regression + Deep Learning. Interpretability

Hands-on Machine Learning

  • Example machine learning project in Robotics
  • Binary classification, decision boundaries, using logistic regression/SVM (one parameter)
  • How to perform a classification experiment. Dataset splitting, learning curves, metrics, comparing results

Regression & Data collection

  • Regression and Data Collection in Robotics
  • Regression methods, least squares fitting
  • Overfitting, cross validation, regularization
  • Collecting (noisy) data, labelling data, outliers
  • High-dimensional data, data augmentation
  • Hyper-parameter optimization (grid search vs random search)


  • Classification in Robotics
  • Parametric vs Non-parametric classifiers
  • Logistic regression
  • Decision tree, forest
  • Bayesian classification: Bayes' rule, naive Bayes, Gaussian Mixture Model
  • k-nearest neighbour
  • SVM, kernel-SVM, dual problem
  • Multi-class classification, metrics (confusion matrix), class imbalance

Unsupervised Learning

  • Unsupervised Learning in Robotics
  • Clustering: K-means, Gaussian Mixture Model, DBSCAN
  • Dimensionality reduction: PCA, Local Linear Embedding (LLE)

Neural Networks

  • Neural Networks in Robotics
  • Multi-Layer Perceptron, gradient descent
  • Neural Networks, and deep learning

Advanced machine learning

  • Outlook: Vanishing gradient problem, DropOut, Optimizers, Data augmentation, AutoEncoder
  • Outlook: Time Series (RNN, LSTM)
  • Outlook: Reinforcement learning


J. Kober

Last modified: 2023-11-03


Credits: 5 EC
Period: 6/0/0/0