Artificial Intelligence and Deep Learning
Artificial Intelligence and Deep Learning
Module Coordinator/Lecturers
Study Programmes
Master's degree programme in Information Systems
Project 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
• 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
Teaching Method
• The course involves interactive lectures with exercises to integrate theoretical knowledge with practical design and analysis skills.
Learning Results
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
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
Assessment Methods
Written exam (60min)