Prerequisite/Recommended prerequisite for participation in 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.

Content, progress and pedagogy of the module

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

Name of exam	Introduction to Artificial Intelligence
Type of exam	Written or oral exam
ECTS	5
Assessment	7-point grading scale
Type of grading	Internal examination
Criteria of assessment	The criteria of assessment are stated in the Examination Policies and Procedures