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

• 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

Teachers

Last modified: 2023-11-03