6 ECTS credits
150 h study time
Offer 1 with catalog number 4004728DNR for all students in the 2nd semester at a (D) Master - preliminary level.
This course introduces the field of Artificial Intelligence and highlights several key techniques. A first part of the course focuses on the history and principles of AI, and how intelligent agents interact with various kinds of environments.
The basic principles of machine learning form the second part of the course, which introduces supervised and unsupervised learning, and classification and regression problems. To obtain insight into the workings of such methods, a number of key techniques such as decision trees, artificial neural networks, and Bayesian probabilistic classifiers are discussed in detail.
A third part deals with classical AI, including problem-solving methods, search, and gameplay strategies such as A* search, minimax and alpha-beta pruning, and the fundamental concepts of knowledge representation and reasoning.
The last part of the course deals with the ethical considerations of AI.
The following topics are treated in this course:
- Introduction and history of AI
- Intelligent Agents
- Machine learning principles
- Learning to Classify
- Artificial Neural Networks
- Stochastic Reasoning
- Searching for Solutions
- Knowledge Representation and Reasoning
- The Ethics of AI
For more information about the specifics of this course, please consult the online learning platform Canvas.
Knowledge and insight
The student is acquainted with the most commonly used AI techniques, including learning algorithms.
The student can recognize, explain, and illustrate key concepts of AI such as intelligent agents, performance metrics and environments.
The student can describe how knowledge is represented and how reasoning can take place in an automated way.
The student can explain and illustrate some commonly used AI techniques and learning algorithms.
The student can correctly trace a learning algorithm on a small data set or environment.
The student can apply evaluation techniques to estimate the performance of the obtained results and to calculate the performance of an algorithm given a concrete application context.
The use of knowledge and insight:
The student can explain the outcome of a given algorithm when applied in a given setting.
The student can choose the appropriate techniques given a concrete learning problem, to apply them correctly and to evaluate the obtained results.
Judgement ability
The student must be able to devise and sustain arguments in favor of against some choice of (learning-) technique for a given problem.
Communication
The student can motivate the chosen approach to specialist and non specialists.
Skills
Students have obtained the skills to autonomously develop, program, analyse, and apply AI techniques to a wide variety of problems.
This course contributes to the following programme outcomes of the Master in Applied Computer Sciences:
MA_A: Knowledge oriented competence
1. The Master in Engineering Sciences has in-depth knowledge and understanding of exact sciences with the specificity of their application to engineering
3. The Master in Engineering Sciences has in-depth knowledge and understanding of the advanced methods and theories to schematize and model complex problems or processes
4. The Master in Engineering Sciences can reformulate complex engineering problems in order to solve them (simplifying assumptions, reducing complexity)
5. The Master in Engineering Sciences can conceive, plan and execute a research project, based on an analysis of its objectives, existing knowledge and the relevant literature, with attention to innovation and valorization in industry and society
6. The Master in Engineering Sciences can correctly report on research or design results in the form of a technical report or in the form of a scientific paper
7. The Master in Engineering Sciences can present and defend results in a scientifically sound way, using contemporary communication tools, for a national as well as for an international professional or lay audience
11. The Master in Engineering Sciences can think critically about and evaluate projects, systems and processes, particularly when based on incomplete, contradictory and/or redundant information
MA_B: Attitude
13. The Master in Engineering Sciences has a critical attitude towards one’s own results and those of others
27. The Master in Applied Computer Science is aware of and critical about the impact of ICT on society.
The final grade is composed based on the following categories:
Written Exam determines 50% of the final mark.
Other Exam determines 50% of the final mark.
Within the Written Exam category, the following assignments need to be completed:
Within the Other Exam category, the following assignments need to be completed:
The exam consists of a closed-book written exam and a project related to machine learning. The realization of the learning outcomes and their understanding and authorship of the project are assessed in a thorough individual oral Q&A session.
Partial results: If the total mark is unsatisfactory, a passing mark on either of the parts will be retained to the second examination period.
A late submission of the project report will result in a mark of 0 for the project work.
This offer is part of the following study plans:
Master of Biomedical Engineering: Standaard traject (only offered in Dutch)
Master in Applied Sciences and Engineering: Applied Computer Science: Standaard traject
Master of Biomedical Engineering: Startplan
Master of Biomedical Engineering: Profile Radiation Physics
Master of Biomedical Engineering: Profile Biomechanics and Biomaterials
Master of Biomedical Engineering: Profile Sensors and Medical Devices
Master of Biomedical Engineering: Profile Neuro-Engineering
Master of Biomedical Engineering: Standaard traject (NIEUW)
Master of Biomedical Engineering: Profile Artificial intelligence and Digital Health