DV2557 Applied Artificial Intelligence

Programme course, 7,5 Higher education credits, Second cycle, autumn semester 2022

This course is part of a programme and cannot be applied.

Applied Artificial Intelligence is a general AI course that both deals
with the history and development of the area artificial intelligent, and
the methods used in order to implement intelligent systems. It takes an
intelligent agent approach to AI and covers subjects such as: machine
learning, expert systems, genetic algorithms, agent technology,
robotics, and planning. The students will face practical excercises
within several of these subjects in order to get a broad view of the
application areas of AI. The course is also an important part in the
prerequisits for advanced courses in the area of AI, among them Adaptive
and Learning systems.

Facts

  • Type of instruction: On campus, day, part-time 50%
  • Period : 2022-August-29 until 2022-October-30
  • Education level: A1N
  • Application: This course is part of a programme and cannot be applied.
  • Language of instruction: The language of instruction is English.
  • Location: Karlskrona
  • Main field of study: Computer Science
  • Course syllabus: Download
  • Welcome letter: This course is part of a programme and has no welcome letter.
  • Entry requirements: Admission to the course requires completed courses 15 ECTS credits in programming, with a minimum of 5 ECTS credits in data structures and algorithms.

Content

The course includes a historical overview of AI-field development, with emphasis on major milestones from an application perspective. Areas covered include

  • knowledge representation
  • expert systems
  • planning
  • pattern recognition
  • natural language processing
  • agent system

Learning outcomes

On completion of course the student will:
• independently be able to demonstrate knowledge of the most basic methods within game AI field and able to reason around its historical development in relation to applications.
• independently and in collaboration with others identify, formulate and divide (AI-related) problem areas and propose solutions with suitable AI-based methods.

  • independently and in collaboration with others develop methods and models to implement and test different solutions to a given (AI-related) problems.

• independently and in collaboration with others evaluate and prioritize different solutions from an overall perspective.

Course literature and other teaching material

Artificial Intelligence ? A modern approach, 3rd ed
Författare: Stuart Russell & Peter Norvig
Förlag: Prentice Hall
Utgiven: 2009, Antal sidor: 1100
ISBN-10: 0-13-604259-7
ISBN-13: 978-0-13-604259-4

Course literature and other teaching material

Artificial Intelligence ? A modern approach, 3rd ed
Författare: Stuart Russell & Peter Norvig
Förlag: Prentice Hall
Utgiven: 2009, Antal sidor: 1100
ISBN-10: 0-13-604259-7
ISBN-13: 978-0-13-604259-4

Learning methods

Course is taught in English in form of lectures which provide foundation in knowledge-related learning. objectives, exercises and laboratory work carried out in smaller groups, which gives students the opportunity to train general abilities and skills and approaches (according to learning aim description).

Work placement

No LIA, but a larger AI-related programming assignment.

Teachers

Planned learning activities

Lectures, seminars exercises and laboratory sessions.

Time allocation

On average, a student should study 200 hours to reach the learning outcomes.
This time includes all the various available learning activities (lectures, self studies, examinations, etc.).
This estimation is based on the fact that one academic year counts as 60 ECTS credits,
corresponding to an average student workload of 1 600 hours. This may vary individually.

Assessments

Component examinations for the course
Code Title ECTS credits Grade
1405 Written examination 4 A-F
1415 Laboration 1 1.5 A-F
1425 Laboration 2 2 A-F

Grading

The course will be graded A Excellent, B Very good, C Good, D Satisfactory, E Sufficient, FX Insufficient, supplementation required, F Fail.

Exams

More information about exams are found in the Student's Portal, where you also enrolls for most exams.


There might be other scheduled examinations. Information regarding these examinations are available in the learning platform Canvas or at other places that the person who is responsible of the course will refer to.

Course Evaluation

The course manager is responsible for the views of students on the course being systematically and regularly gathered and that the results of the evaluations in various forms affect the form and development of the course.

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