C19 Artificial Intelligence and Deep Learning — University of Liechtenstein

Semester:SS 21

Type:Module

ECTS-Credits:3.0

Scheduled in semester:2

Semester Hours per Week / Contact Hours:30.0 L / 22.5 h

Self-directed study time:67.5 h

Module coordination/Lecturers

Master's degree programme in Information Systems (01.09.2019)

Artificial Intelligence and Deep Learning covers the basics of artificial intelligence and deep learning and recent technological trends. The course covers five primary topics:
Fundamentals of artificial intelligence
Fundamentals of deep learning, network design, and training
Convolutional neural networks, illustrated through image recognition
Recurrent neural networks, illustrated through text mining
Deep reinforcement learning - Learning to play games and beyond: Google’s AlphaGo

After successful completion of the course, students will:understand the basic concepts and methods of artificial intelligence and deep learningbe able to identify suitable applications for artificial intelligence and deep learningbe able to select, use, and adjust existing models and methods for a given task or data set

The course involves interactive lectures with exercises to integrate theoretical knowledge with practical design and analysis skills.The e-learning platform Moodle is used throughout the course to disseminate course material and for information and discussion.

Though not mandatory, students should attend the Data Science course, which also takes place in the second semester, in parallel. Any other basic course on data science, data mining or machine learning is also accepted. Exceptions are only possible after consultation with the lecturer and the study program management.

Compulsory reading:Russel, S., & Norvig, P. (2009). Artificial Intelligence: A Modern Approach (3rd ed.). Harlow, UK: Pearson. - Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. Cambridge, MA: The MIT Press.