Course syllabus - Artificial Intelligence 2

Objectives

The course aims at deepening the knowledge in artificial intelligence by looking into practical methods for problem definition and knowledge representation. Furthermore, to introduce logic programming and paradigms suitable for when fast execution and memory safety are desirable properties. The course also aims to deepen the knowledge regarding ethical and gender equality analysis of AI systems.

Learning outcomes

After completing the course, the student shall be able to:

1. explain and apply core methods in symbolic and population-based artificial intelligence,

2. analyse a given problem and select and justify appropriate methods and parameters when developing AI algorithms,

3. assess how different representations of knowledge affect model performance and resource efficiency and also

4. analyze and discuss overarching perspectives on artificial intelligence, such as ethics and gender equality.

Course content

The course covers classical methods in artificial intelligence with a focus on symbolic and population-based AI. The content includes logic programming, evolutionary algorithms, reasoning under uncertainty, and combinations of symbolic and numerical representations. The course also includes an ethics and gender equality analysis of agent-based AI systems.

Specific requirements

30 credits in computer science, which includes Programming 7.5 credits, Data Structures, Algorithms, and Programme Development 7.5 credits, and Artificial Intelligence 7.5 credits. In addition, vector algebra 7.5 credits is required.

Examination

LAB1, Laboratory session 1, a series of laboratory sessions demonstrated according to instructions, 7.5 credits, examines the learning outcomes 1–7, marks Fail (U) or Pass (G).

A student who has a certificate from MDU regarding disability study support, can request adaptions for the examination. It is the examiner who takes decisions on any adaptions, based on the certificate and other conditions.

Grade

Two-grade scale

Interim Regulations and Other Regulations

The course completely overlaps with CDT312/DVA340/DVA349 Artificial Intelligence and DVA255/DVA265 Artificial Intelligence 2. The courses Artificial Intelligence 1 and Artificial Intelligence 2 can not be included in the same degree as CDT312/DVA340/DVA349 Artificial Intelligence.

The course contributes to fulfill the degree requirement of at least 75 credits in the main area of computer science with the focus of intelligent systems for technology bachelor's degree in computer science with the focus of intelligent systems.