Overview

1. The exam is viva voce.
2. Duration Normally 20 min. May extend up to a maximum of 30 min. if necessary.
3. The student manages the first seven minutes of the exam to make their case to demonstrate that the Learning Outcomes have been achieved.
4. The student is free to bring any material desired to serve as a demonstration.
   However, it is the candidate’s understanding which will be assessed, and not prepared materials, and the student must expect in-depth questions about their demonstrations.
5. The remaining 2/3 of the exam is for further questions from the examiners to make sure that all the learning outcomes are covered.

Learning Outcomes

Knowledge: Upon completion of the course, students should be able to

• K1 describe AI in terms of the analysis and design of intelligent agents or systems that interact with their environments
  
• K2 explain relevant AI terminology, models, and algorithms used for problem solving

Skills: Upon completion of the course, students should be able to

• S1 model problems in suitable state space depending on choice of solution method
• S2 simulate models and solve problems by means of AI methods, e.g., search algorithms or computational intelligence
  
• S3 a nalyse models, AI methods, and simulation results

General competence: Upon completion of the course, students should be able to

• C1 read and understand scientific publications and textbooks on AI and reformulate the presented problems, choice of methods, and results in a short, concise manner
• C2 discuss and communicate advantages and limitations of selected AI methods for problem solving

Interpretation of Grades and Contents

Main Topics in the Taught Programme

1. Intelligent Agents
   • What is intelligence? Rationality?
   • The role of Intelligent Agents
   • K1, K2
2. Search Algorithms (mostly based on tree traversal)
   • K2, S1, S2, S3, C1
3. Genetic Algorithms (and optimisation)
   • S2, S3
4. Machine Learning and Reinforcement Learning
   • S2, S3, C2
5. Ethics and Philosophy
   • Particularly discussion of «advantages and limitations» (C2)
   • C1, C2

Interpretation of Grade C

The Grade C represents solid working knowledge of the most important techniques in the syllabus, including the skills to select, implement, and evaluate the techniques, and an awareness of limitations and ethical caveats. This is the level required to carry out independent work in the field.

The C Candidate demonstrates

• a broad overview of all the five main topics
• coverage of all the learning outcomes
• cursory knowledge of all five main topics
• depth understanding of a couple of favourite algorithms,
  • representing at least two of the three topics 2/3/4.
  • going beyond the toy examples in each topic
  • where they are able to implement and evaluate and apply to appropriate problems.
  • knowledge of relevant applications of the algorithms studied
• sufficient theoretical understanding to vary and adapt algorithms and solution techniques to different use cases
• the reflection to relate knowledge and competencies to own needs and future career.
  • Why is the contents of this module worth learning?

Interpretation of Grade A

The Grade C represents broad and deep understanding exceeding expectations, as it says in the national guidelines:

An excellent performance, clearly outstanding. The candidate demonstrates excellent judgement and a very high degree of independent thinking.

In addition to the requirement for a C, an A Candidate demonstrates

• depth understanding of algorithms from all the three main topics 2/3/4.
• solid theoretical understanding spanning all five main topics

Note that this level of understanding requires reading beyond what we have discussed in class. Still, students are not expected to cover all the algorithms discussed, but those algorithms that are studied should be very well understood.

Interpretation of Grade E

The Grade E represents superficial working knowledge, sufficient to make a useful contribution to a team, but insufficient for indepedent work.

The E Candidate demonstrates

• some understanding of each of the five main topics
• some understanding towards each of the learning outcomes
• understanding and experience with a couple of favourite algorithms,
  • representing at least two of the three topics 2/3/4.
  • with sufficient knowledge and skill to implement solutions
• knowledge of relevant applications of the algorithms studied

Grades D and B

Grades D and B are intermediate grades, showing some characteristics of both the lower and the higher grade.

Format of the Exam

The candidate has the first seven minutes to make a case for a grade, demonstrating the skills and competencies required. It is particularly important in this part of the exam to cover favourite algorithms and other highlights.

The student may bring demonstrations to use in the first part, but assessment is based on the understanding of the techniques demonstrated and not the quality of the demonstration itself.

Do not make slide shows to display theory more advanced than what you are able to discuss in conversation. In an exam you want to appear to know a lot more than what you are able to tell in the time allotted! That image is immediately broken if you have more on the slides than you have in your head.

The remaining two thirds of the exam is managed by the examiner. This is used to make sure that the student and the examiners agree that all the necessary material is covered.

Note that it is the student’s responsibility to promote the highlights of their understanding, including depth on some selected topics. It is the examiner’s responsibility that sufficient breadth is covered.