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Artificial Intelligence (DLMAIAI01)

Kursnummer:

DLMAIAI01

Kursname:

Artificial Intelligence

Gesamtstunden:

150 h

ECTS Punkte:

5 ECTS

Kurstyp: Pflicht, Wahlpflicht

Zu Details beachte bitte das Curriculum des jeweiligen Studiengangs

Kursangebot : WS, SS

Course Duration : Minimum 1 semester

Zugangsvoraussetzungen:

None

Kurskoordinator(en) / Dozenten / Lektoren:

Siehe aktuelle Liste der Tutoren im Learning Management System

Bezüge zu anderen Modulen:

Siehe Modulbeschreibung

Beschreibung des Kurses:

The quest for artificial intelligence has captured humanity’s interest for many decades and has been an active research area since the 1960s. This course will give a detailed overview of the historical developments, successes, and set-backs in AI, as well as the development and use of expert systems in early AI systems.

In order to understand cognitive processes, the course will give a brief overview of the biological brain and (human) cognitive processes and then focus on the development of modern AI systems fueled by recent developments in hard- and software. Particular focus will be given to discussion of the development of “narrow AI” systems for specific use cases vs. the creation of general artificial intelligence.

The course will give an overview of a wide range of potential application areas in artificial intelligence, including industry sectors such as autonomous driving and mobility, medicine, finance, retail, and manufacturing.

Course Objectives and Outcome:

On successful completion of this course, students will be able to:

  • remember the historical developments in the field of artificial intelligence.
  • analyze the different application areas of artificial intelligence.
  • comprehend expert systems.
  • apply Prolog to simple expert systems.
  • comprehend the brain and cognitive processes from a neuro-scientific point of view.
  • understand modern developments in artificial intelligence.

Teaching Methods:

The learning materials include printed and online course books, vodcasts, online knowledge tests, podcasts, online tutorials, and case studies. This range of learning materials is offered to students so they can study at a time, place, and pace that best suits their circumstances and individual learning style.

Course Content:

1. History of AI

1.1 Historical Developments

1.2 AI Winter

1.3 Notable Advances in AI

2. Expert Systems

2.1 Overview Over Expert Systems

2.2 Introduction to Prolog

3. Neuroscience

3.1 The (Human) Brain

3.2 Cognitive Processes

4. Modern AI Systems

4.1 Recent Developments in Hard- and Software

4.2 Narrow vs General AI

4.3 NLP and Computer Vision

5. AI Application Areas

5.1 Autonomous Vehicles & Mobility

5.2 Personalized Medicine

5.3 FinTech

5.4 Retail & Industry

Literatur:

  • Bear, F., Barry, W., & Paradiso, M. (2006). Neuroscience: Exploring the brain (3rd ed.). Baltimore, MD: Lippincott Williams and Wilkins.
  • Bratko, I. (2011). Prolog programming for artificial intelligence (4th ed.). Hoboken, NJ: Pearson.
  • Jackson, P. (1998). Introduction to expert systems (3rd ed.). Chicago, IL: Addison Wesley Longman.
  • Nilsson, N. (2009). The quest for artificial intelligence. Cambridge: Cambridge University Press.
  • Russel, S., & Norvig, P. (2009). Artificial intelligence: A modern approach (3rd ed.). Malaysia: Pearson.

A current list with course-specific compulsory reading, as well as references to further literature, is stored in the Learning Management System.

Prüfungszugangsvoraussetzung:

  • Depending on the course: Completion of online knowledge tests (approx. 15 minutes per unit, pass / not pass)
  • Course evaluation

Prüfungsleistung:

Exam, 90 min.

Student Workload (in hours): 150

Self-study: 90
Self-testing: 30
Tutorials: 30