Content, progress and pedagogy of the module

The module is based on knowledge achieved in the modules Linear algebra, Calculus, Data structures and algorithms, Real-time systems and programming languages.

Learning objectives

Knowledge

• Have knowledge about what encompasses artificial intelligence (AI) and its main applications
• Have knowledge about the basic algorithms and heuristics used for searching in graphs such as shortest path, A* and the travelling salesman problem
• Have knowledge about optimisation techniques such as gradient based methods for convex functions and metaheuristic methods
• Have knowledge about the concepts of supervised and unsupervised machine learning techniques, about perceptron learning and multilayer perceptron learning for classification
• Have knowledge about crisp logic systems and knowledge representation
• Have knowledge about image processing and computer vision algorithms

Skills

• Be able to design and implement AI based algorithms and heuristics for searching, optimisation or knowledge representation in a modern computer language such as Python or C++
• Be able to develop computer programs using libraries to implement machine learning based systems for specific applications
• Be able to design AI based computer programs for computer vision and reasoning using 1st order logic

Competences

• Independently be able to apply AI to solve problems in robotics and computer vision
• Independently develop an AI based system solutions for a specific application domain
• Have a fundamental understanding of the techniques used in AI

Type of instruction

Lectures with exercises, possibly supplemented with e-learning as stated in § 17 in the BSc curriculum and §18 in the BE curriculum.

Extent and expected workload

Since it is a 5 ECTS project module, the work load is expected to be 150 hours for the student.

Exam

Exams