Applied machine learning with project

Data representation: representation of various data sources such as images, sound and text, current techniques for processing data.

Unsupervised learning: various clustering algorithms, reduction of dimensions, and other current methods.

Supervised learning: including logistic regression and different types of neural networks.

Knowledge

The candidate can give an account of:

• different ways of representing data
• different methods for grouping and classifying data
• which machine learning methods are appropriate to use for given problems
• limitations of machine learning

Skills

The candidate can:

• create full-fledged machine learning solutions using a framework
• use representation algorithms that make it easier for machine learning methods to give better results for a given data set
• select and adapt a machine learning method that is relevant to a given problem
• assess whether machine learning methods can give good results for a given problem based on a given data set

General competence

The candidate must be able to find and adapt solutions to new problems based on previous applications of machine learning.

Lectures, exercises and project.