Recommended prerequisite for participation in the module

Content, progress and pedagogy of the module

The course gives a comprehensive introduction to machine learning, covering learning theory, methods and applications. The objective is realized by presenting methods and tools proven valuable and by addressing specific application problems.

Learning objectives

Knowledge

Students must have knowledge about:

• Supervised learning methods including logistic regression, support vector machines, and decision trees.
• Unsupervised learning methods including K-means, Gaussian mixture model, and EM algorithm.
• Feed-forward, convolutional and recurrent neural networks, transfer learning.
• Probabilistic graphical models, hidden Markov models.
• Reinforcement learning.
• Bayesian decision theory, bias and variance trade-off and cross-validation.

Skills

• Must be able to apply the taught methods to solve concrete engineering problems.
• Must be able to evaluate and compare the methods within a specific application problem.

Competences

• Must have competences in analyzing a given problem and identifying appropriate machine learning methods to the problem.
• Must have competences in understanding the strengths and weaknesses of the methods.

Type of instruction

The course will be taught a combination of lectures, demos of applications, exercises and mini-project

Exam

Prerequisite for enrollment for the exam

• Submission of mini-project report

Exams