Content, progress and pedagogy of the module

The course gives a comprehensive introduction to machine learning, which is a field concerned with learning from examples and has roots in computer science, statistics and pattern recognition. The objective is realized by presenting methods and tools proven valuable and by addressing specific application problems.

Learning objectives

Knowledge

• Must have knowledge about supervised learning methods including K-nearest neighbors, decision trees, linear discriminant analysis, support vector machines, and neural networks.
• Must have knowledge about unsupervised learning methods including K-means, Gaussian mixture model, hidden Markov model, EM algorithm, and principal component analysis.
• Must have knowledge about probabilistic graphical models, variational Bayesian methods, belief propagation, and mean-field approximation.
• Must have knowledge about Bayesian decision theory, bias and variance trade-off, and cross-validation.
• Must be able to understand reinforcement learning.

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

Type of instruction

As described in the introduction to Chapter 3.

Exam

Exams