Introduction to Artificial Intelligence

Lecturers

Details

Course type
Lectures
Credits
5 CP
Term
Summer Term 2022
E-Learning
moodle course available

Dates

Lecture
Takes place every week on Friday from 10:15 to 11:45 in room HGD 20.
First appointment is on 08.04.2022
Last appointment is on 15.07.2022
Exercise
Takes place every week on Friday from 12:15 to 13:45 in room HGD 20.
First appointment is on 08.04.2022
Last appointment is on 15.07.2022

Lecturers: Laurenz Wiskott, Sen Cheng, Gregor Schöner, Maribel Acosta, Asja Fischer, Christian Straßer, Anand Subramoney

Enrollment: Students from the RUB Bachelor study programs (Applied) Computer Science simply enroll in the Moodle course and participate. Students from the TU Dortmund Master study program Data Science contact their coordinator Daniel Horn. Others send an email to Laurenz Wiskott. You might also have to enroll for this course in your examination office, but this is something you'll have to figure out yourself.

Credits: 5 CP (3 CP for Master-students from technical study programs; 0 CP for (Applied) Computer Science Master-students; technical study programs include IT-Security, Maschinenbau, Data Science (TU Do), Automation & Robotics (TU Do) ...)

Workload: 150 h (90 h for Master-students from technical study programs)

Semester: second semester Bachelor

Cycle (Turnus): each SS

Duration (Dauer): 1 semester

Contact time (Kontaktzeit): 4 SWS (60 h)

Self studies (Selbststudium): 90 h

Group size (Gruppengröße): ca 300

Language: English.

Requirements: Basic knowledge of calculus and linear algebra.

Learning outcomes (Lernziele): 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 above aspects in English.

Content (Inhalt): 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, and robotics.

Teaching format (Lehrformen): This course is given with the flipped/inverted classroom concept. The students work through online material beforehand and this will then be deepened in the contact sessions, which will be used for an interactive exchange between students and with the lecturer in a flexible format.

Exam (Prüfungsformen): There will be a final written online-exam through a separate exam Moodle on the 05.08.  and 30.09. for 90 minutes within 11:00-13:00. You can take the exam from home or any other convenient location, but you  need a stable internet connection and working camera. This is an open book exam, thus you may use any tools (e.g. lecture notes or Wikipedia) except communication with other people. Registration for the exam with us happens at the end of the course, you will receive instructions on that in due time. You might also have to register for this exam in your examination office, but this is something you'll have to figure out yourself. There are no prequisits for the exam, like 50% points in tutorials or the like.

Condition for granting the credit points (Voraussetzungen für die Vergabe von Kreditpunkten): Passing grade on final written exam.

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