Single-Course English 5 ECTS

Project in Intelligent Systems

Overall Course Objectives

This course aims to provide participants with the fundamental competences required to conceive, design and implement intelligent and distributed control systems combining data-driven and knowledge-based techniques. The course work offers participants an application-oriented introduction to machine learning and declarative methods of artificial intelligence. Course participants will learn the practical aspect of intelligent systems with respect to use-cases and top level design to design.

Learning Objectives

  • Identify and discuss situation awareness, decision, and control tasks in an intelligent systems problem.
  • Apply machine learning methods to data modeling problems in intelligent systems and evaluate their effectiveness.
  • Work with a large data set, perform data exploration and feature extraction to derive knowledge from data in a real-world automation problem.
  • Explain structured knowledge representation techniques and related modeling principles, and select suitable knowledge representations for integration in an intelligent systems solution.
  • Describe suitable uses of logic-based and declarative methods applied to the solution of decision making problems in intelligent systems.
  • Explain and select appropriate methods as well as the corresponding data models and knowledge representations as part of a design problem.
  • Analyse an intelligent systems design problem, communicate the design idea, formulate specifications and test requirements.
  • Design, implement and evaluate an operational prototype of an intelligent system using data-driven and declarative programming techniques applied to a distributed systems problem.

Course Content

Introduction to intelligent systems problems, task analysis, systems development approaches & architecture elements; applications of intelligent systems in automation, Internet of Things (IoT) and Smart Grid / Energy problems.
Distributed systems basics: concurrency, communication, and debugging.
Structured knowledge representation (e.g. ontologies) and applications to declarative and logic-based programming methods (e.g. rule-based logic, graph-search, … ).
Handling large data sets; data visualization; applied statistical learning methods. Quality of data-driven models.

Recommended prerequisites

34666, The course is based on the project description that was an assignment in course 34366.

Teaching Method

Project work – individually or in groups



The course is also offered in the June 3-week period as course 34372.

It is recommended to consider programming prerequisites of this course, and to contact the course responsible if in doubt.

Limited number of seats

Minimum: 6.

Please be aware that this course will only be held if the required minimum number of participants is met. You will be informed 8 days before the start of the course, whether the course will be held.

See course in the course database.





3 weeks




DTU Lyngby Campus

Course code 34367
Course type Candidate
Semester start Week 1
Semester end Week 35
Days Mon-fri 8:00-17:00

7.500,00 DKK

Please note that this course has participants limitation. Read more