Applied Machine Learning

Education facts

Code: DVGC27

The course provides an introduction to machine learning with a focus on applied deep machine learning. The first part of the course covers the basics of machine learning, including relevant theory, terminology, and principles, and introduces both classical and deep machine learning. Instruction is mainly in the form of lectures and self-study of educational materials based on open-access sources. Some parts involve flipped classroom activities. The second part of the course focuses on applied deep machine learning through a number of practical exercises and combines lectures on necessary theory with laboratory sessions. The aim is to enable students to explore problem solution independently through using popular libraries and tools with open source code for deep machine learning. The course concludes with a laboratory task focused on deep machine learning which is presented orally in groups.

Entry requirements

60 ECTS credits completed including Data Structures and Algorithms, 7.5 ECTS credits, and Discrete Mathematics, 7.5 ECTS credits, or equivalent.

Education offerings

Each offering has its own dates and conditions. Closed offerings are retained as history and do not mean that a new application is open.

• Applied Machine Learning

  Karlstad University

  Karlstad

  Start date: 9 November 2026

  End date: 17 January 2027

  Pace of study: 50 %