DM879: Artificial Intelligence
The student is expected to have basic understanding of mathematical proofs and programming, obtainable e.g. by having followed DM549 Discrete methods for computer science or MM537 Introduction to Mathematical Methods and DM536/DM550 Introduction to Programming or DM562 Scientific Programming.
- provide knowledge of a selection of specialized models and methods developed in computer science based on the highest international research, including topics from the subject's research front
- provide knowledge of computer science models and methods intended for applications in other professional areas
- provide the ability to describe, analyze and solve advanced computer science problems using the learned models
- provide the ability to develop new variants of the learned methods where the specific problem requires it
- provide competence to plan and carry out scientific projects at a high professional level, including managing work and development situations that are complex, unpredictable and require new solutions
Expected learning outcome
At the end of this course, the student is expected to have the following competences:
- outline the basic logic and probabilistic principles of problem solving, reasoning, learning, and decision making under uncertainty;
- describe in detail the fundamental algorithms of searching, reasoning, learning and decision making under uncertainty covered in the curriculum of the course;
- assess the applicability of basic problem solving, reasoning and learning techniques to different problems that resemble those seen in the lectures;
- develop intelligent systems to solve concrete computational problems.
- Overview of Artificial Intelligence
- Search techniques
- Knowledge, reasoning and planning
- Probabilistic reasoning
- Machine learning
- Natural language processing
Exam element a)
Project and written exam
To be announced during the course.
The exam consists of a group project and an individual written exam. The written exam includes a number of questions about the project, the aim of which is to evaluate the student's individual contribution to the project. The final grade combines the performance of both components.
The examination form for re-examination may be different from the exam form at the regular exam.
Indicative number of lessons
- Intro phase: 28 hours
- Skills training phase: 28 hours, including 20 hours tutorials and 8 hours laboratory exercises
Activities during the study phase: Solving small assignments, individually or in small groups.
|Luís Cruz-Filipeemail@example.com||Institut for Matematik og Datalogi, Datalogi|
|Marco Chiarandinifirstname.lastname@example.org||Institut for Matematik og Datalogi, Datalogi, Datavidenskab & Statistik|
|08 - 09|
|09 - 10|
|10 - 11|
|11 - 12|
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|15 - 16|