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Curriculum

Modul CS3204-KP04, CS3204

Artificial Intelligence 1 (KI1)

Duration:


1 Semester
Turnus of offer:


each summer semester
Credit points:


4
Course of studies, specific field and terms:
  • Bachelor Robotics and Autonomous Systems 2020 (compulsory), Robotics and Autonomous Systems, 6th semester
  • Bachelor Computer Science 2019 (optional subject), major subject informatics, Arbitrary semester
  • Bachelor MES 2020 (optional subject), computer science / electrical engineering, 3rd semester at the earliest
  • Bachelor Media Informatics 2020 (optional subject), computer science, 5th or 6th semester
  • Bachelor Medical Informatics 2019 (optional subject), computer science, 4th to 6th semester
  • Bachelor MES 2014 (optional subject), computer science / electrical engineering, 3rd semester at the earliest
  • Bachelor Computer Science 2016 (optional subject), major subject informatics, Arbitrary semester
  • Bachelor Computer Science 2016 (compulsory), Canonical Specialization Web and Data Science, 6th semester
  • Bachelor Robotics and Autonomous Systems 2016 (compulsory), Robotics and Autonomous Systems, 6th semester
  • Bachelor IT-Security 2016 (optional subject), computer science, Arbitrary semester
  • Bachelor Biophysics 2016 (optional subject), computer science, 6th semester
  • Bachelor Medical Informatics 2014 (optional subject), computer science, 5th or 6th semester
  • Bachelor Media Informatics 2014 (optional subject), computer science, 5th or 6th semester
  • Bachelor Computer Science 2014 (optional subject), central topics of computer science, 6th semester
  • Bachelor Computer Science 2014 (compulsory), specialization field robotics and automation, 6th semester
  • Bachelor Medical Informatics 2011 (optional subject), Applied computer science, 4th to 6th semester
  • Bachelor CLS 2010 (optional subject), computer science, 6th semester
  • Bachelor MES 2011 (optional subject), medical engineering science, 6th semester
  • Bachelor Computer Science 2012 (compulsory), specialization field robotics and automation, 4th semester
  • Bachelor Computer Science 2012 (optional subject), central topics of computer science, 5th or 6th semester
Classes and lectures:
  • Artificial Intelligence (exercise, 2 SWS)
  • Artificial Intelligence (lecture, 2 SWS)
Workload:
  • 55 Hours private studies
  • 45 Hours in-classroom work
  • 20 Hours exam preparation
Contents of teaching:
  • Part 1: Search strategies As an introduction and a prerequisite for most of the principles of artificial intelligence search strategies are introduced and explained. We will introduce uninformed, informed, local search, adversial search as well as heuristic search. The concept of agents will be presented.
  • Part 2: Learning and reasoning Revision of the foundations of mathematical logic and probability. Principles of machine learning (supervised and unsupervised) are introduced. An introduction to fuzzy logic is also included.
  • Part 3: Applications of artificial intelligence Typical applications in the fields or robotics, machine vision, and industrial image and data processing are identified. Ethical issues and risks of the development of artificial intelligence are discussed.
Qualification-goals/Competencies:
  • The students are able to handle scope-oriented tutorials with a mathematical background in a team, and timely.
  • They have developed an understanding for the benefits and disadvantages of the different search and problem solving techniques.
  • The students are in a position to choose and apply independently appropriate algorithms for search and learning issues.
  • They have gained an insight into the complex development of systems with artificial intelligence and the distinction of its various forms.
  • The students have an understanding of the risks and possible technological consequences of the development of systems with strong AI.
Grading through:
  • portfolio exam
Requires:
Responsible for this module:
  • Prof. Dr. rer. nat. Floris Ernst
Teachers:
  • MitarbeiterInnen des Instituts
  • Prof. Dr. rer. nat. Floris Ernst
Literature:
  • G. Görz (Hrsg.): Handbuch der Künstlichen Intelligenz - München: Oldenbourg Wissenschaftsverlag, 2003
  • C-M. Bishop: Pattern Recognition and Machine Learning - Springer Verlag, 2007
  • Russell/Norvig: Artificial Intelligence: a modern approach - (3rd Ed.), Prentice Hall, 2009
  • Mitchell: Machine Learning - McGraw-Hill, 1997
  • Luger: Artificial Intelligence: Structures and Strategies for Complex Problem Solving - (6th Ed.), Addison-Wesley, 2008
Language:
  • offered only in German
Notes:

Admission requirements for taking the module
- None (the competences of the modules mentioned under “Requires” are needed for this module, but are not a formal prerequisite).

Admission requirements for participation in module examination(s):
- Successful completion of exercises as specified at the beginning of the semester.

Moduel Exam(s):
- CS3204-L1: Artificial Intelligence, Portfolio examination, 100% of the module grade

Note: The portfolio examination consists of: 70 points in the form of a written examination at the end of the semester, 15 points in the form of semester-accompanying programming tasks (group and individual performance), 15 points in the form of semester-accompanying e-tests (individual performance)

Letzte Änderung:
3.9.2024