Single-Course English 5 ECTS

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 theoretical aspect of intelligent systems with respect to use-cases and top level design to design.

Learning Objectives

  • Identify a real-world problem and propose an intelligent system that can be used to help with this problem.
  • Identify and discuss situation awareness, decision, and control tasks in an intelligent system and identify the hardware and/or software requirements.
  • Discuss whether machine learning methods are applicable for a proposed intelligent system, and (if relevant) propose a suitable machine learning algorithm/method and evaluate its 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 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 operational prototype of an intelligent system using data-driven techniques applied to a distributed systems problem.
  • Write a project proposal for an intelligent system that is intended to solve a real-world problem and discuss how the functionality of the implementation can be tested and verified and how critical parts/elements can be identified.
  • Discuss the quality and availability of data for an intelligent system and how GDPR rules can affect the implementation or operation of an intelligent system.
  • Give appropriate feedback on a project proposal

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

Experience with script programming (e.g. MATLAB, Python, etc.) and object-oriented programming (e.g. Java, C++, or python etc.).

Teaching Method

Lectures, exercises and group work. Project planning work i groups


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.





13 weeks




DTU Lyngby Campus

Course code 34366
Course type Candidate
Semester start Week 35
Semester end Week 48
Days Tues 18-22

7.500,00 DKK

Please note that this course has participants limitation. Read more