Introduction to Artificial Intelligence

Content:

This course gives an overview over representative methods in artificial intelligence: formal logic and reasoning, classical methods of AI, probabilistic reasoning, machine learning, deep neural networks, computational neuroscience, neural dynamics, perception, natural language processing, robotics.

Learning Outcomes:

After successful completion of this course, students will be able to

  • summarize a number of fundamental methods in artificial intelligence,
  • explain their mathematical basis and algorithmic nature,
  • apply them to simple problems,
  • decide which methods are suitable for which problems, and
  • communicate about the all that in English.

Examination:

Condition for granting the credit points: Passing grade on final written exam (120 minutes)

Note: Bachelor students earn 5 CP by successfully passing the exam. For Master students the following applies: 3 CP for Master students from technical study programs; 0 CP for (Applied) Computer Science Master students; technical study programs include Maschinenbau, Data Science (TU Do), Automation&Robotics (TU Do)...

Lecturers

Details

Course type
Lectures
Credits
5 CP
Term
Summer Term 2024

Dates

Lecture
Takes place every week on Friday from 10:00 to 12:00 in room HGD 20.
First appointment is on 12.04.2024
Last appointment is on 19.07.2024
Exercise
Takes place every week on Friday from 12:00 to 14:00 in room HGD 20.
First appointment is on 12.04.2024
Last appointment is on 19.07.2024

Requirements

Basic knowledge of calculus and linear algebra.

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.

Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany

Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210