MAT301 – Artificial Intelegence

Module designation

Artificial Intelegence (MAT301)

Semester(s) in which the module is taught

5^th

Person responsible for the module

Auli Damayanti, M.Si.

Language

Indonesian

Relation to curriculum

Compulsory / elective / specialisation

Teaching methods

Lecture and lesson.

Workload (incl. contact hours, self-study hours)

3×170 minutes (3×50 minutes lecture and lesson, 3×60 minutes structural activities, 3×60 minutes self-study) per week for 16 weeks

Credit points

3 CP (4,8 ECTS)

Required and recommended prerequisites for joining the module

Algorithms and Programming (MAT203)

Module objectives/intended learning outcomes

General Competence (Knowledge)

Implement and make applications of artificial intelligence in all fields.

Specific Competence: students are able to

1.      Explain about artificial intelligence and scope.

2.      Analyze problems and can solve space problems with blind search method

3.      Solve problems using a variety of heuristic search techniques.

4.      Represent knowledge using a variety of techniques knowledge representation

5.      Solve the problem by using the rules and methods of inference

6.      Solve problems using methods of uncertainty

7.      Apply Artificial Intelligence to solve various cases

Content

Introduction to Artificial Intelligence, Problems, Problem Spaces, and search, Heuristic Search Techniques, Knowledge Representation, Inference Methods, Uncertainty, Artificial Intelligence Applications

Examination forms

include essay test (quiz, middle exam ) and group presentation (final exam)

Study and examination requirements

Students are considered to pass if they at least have got a final score 40 (D).

Final score is calculated as follows: 10% softskill+11.25% assignment + 14.5% Quiz + 18.75% midterm + 50.5% final exam (project).

Final index is defined as follow:

Reading list

1.       William Siler and James J. Buckley, “Fuzzy Expert System and Fuzzy Reasoning”, Wiley-Interscience, 2005

2.      Laurene Fauset, “Fundamental of Neural Network”, Prentice Hall, 2000.