Introduction to Artificial Intelligence

Content:

This course gives an overview of 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 above in English.

Lecturers

Details

Course type
Lectures
Credits
5 CP
Term
Summer Term 2024
E-Learning
moodle course available

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.

Further Lecturers (sorry, for technical reasons, not all lecturers can be displayed above): Ivan Habernal, Nils Jansen, Christian Straßer, Bilal Zafar

Degree Program: Bachelor Applied Computer Science, Bachelor Computer Science

Semester: fourth semester

Credits: 5 CP

Workload: 150 h

Cycle (Turnus): each summer semester

Duration (Dauer): 1 semester

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

Self studies (Selbststudium): 90 h

Group size (Gruppengröße): ca 150

Language: English.

Enrollment: All students must enroll in the Moodle course to receive the class materials and announcements. Students from the Bachelor programs (Applied) Computer Science at RUB do not need prior permission to attend the class. 

Students from other degree programs: Students from other degree programs are welcome to attend to this class, but some rules differ. Students from the Master program Data Science at TU Dortmund should contact their program office (dekanat.statistik@tu-dortmund.de) to enroll. Others should send an email to Sen Cheng. In addition, you might have to enroll for this course with your examination office according to the rules of your degree program, only they will be able to advise you. Your examination office might also decide to award a different number of credit points than listed above for this class.

Final Exam: The exam will be administered on two dates after the lecture period in the summer semester. The tentative dates are 12.08.2024 and 30.09.2024. Students are free to choose either of the two dates. Keep in mind that the next opportunity to take the exam will be about one year later.

The written exam will be 90 minutes long within a 100 minute time slot. The exam will be digital and must be taken in person in a computer lab on the RUB campus. It will be a closed book exam, thus you are not allowed to consult any materials. There are no prerequisits for taking the exam.

Every student who wishes to take the exam must register in two places.

  1. In the corresponding course in the RUB online exam system (https://online-exam.ruhr-uni-bochum.de/). You will take the exam in this Moodle environment. 
    Important: This is another instance of Moodle that looks the same, but it is different from the Moodle course that we use to share class materials and announcements! 
  2. With the examination office of the Faculty of Computer Science. If you are a student of our faculty, mathematics or physics,  register via FlexNow. Otherwise, send an email to informatik-pruefungsamt@rub.de 

A mock exam will be available towards the end of the course. It will be shorter than the final exam, but it will contain at least one question from every instructor. The main purpose is to give you a good impression of the style of the exam and therefore facilitate exam preparation.

Condition for granting the credit points: To pass this class and receive credit, you need a score of at least 50 out of 100 on the final exam. The score S differs from the points P that you achieved on the final exam. The default mapping is: S=100*(P-C)/(M-C), where C is the expected number of points one would get by pure guessing and M the maximum possible number of points. For example, with pure guessing, i.e. P=C, you would get a score of 0 on average and with perfect answers, i.e. P=M, you get a score of 100.

About the INI

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.

Contact

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

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

sekretariat@ini.rub.de
www.ini.rub.de