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
- Assoz. Prof. Dr. Johannes Schneider
(Modulleitung)
Curricula
Master's degree programme in Information Systems (01.09.2019)Description
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
Learning Outcomes
After successful completion of the course, students will
Professional competence
• understand the basic concepts and methods of artificial intelligence and deep learning
• be able to identify suitable applications for artificial intelligence and deep learning
Methodological competence
• select, use, and adjust existing models and methods for a given task or data set
Personal competence
• critically reflect on analytical outcomes
• be able to improve and mitigate self-inflicted errors
Technological competence
• be able to use a deep learning framework such as Keras
Qualifications
Lectures Method
• The course involves interactive lectures with exercises to integrate theoretical knowledge with practical design and analysis skills.
Admission Requirements
• Students should attend the Data Science course, which is held concurrently in the same semester.
Literature
• 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.
Exam Modalities
Written exam (60min)