Applications of AI

About This Course

Over the course of 8 weeks, students will explore how AI is applied across Healthcare, Social Media, Finance, Human Resources, and IoT & Smart Farming, integrating conceptual foundations with practical demonstrations. A central component of this module is the use of large-scale, publicly available datasets from each of these domains. Together, we will learn to clean and preprocess data, explore and interpret relevant features, and address domain-specific challenges in handling heterogeneous data sources.

Building on this foundation, we will train, compare, and evaluate both classical machine learning models and deep neural networks, gaining a systematic understanding of their performance across different tasks. The full AI pipeline (from raw data to predictive insight) will be critically assessed, with emphasis on model evaluation, interpretability, and the generation of deep insights within each application domain.

Learning Objectives

By the end of this course, you will be able to:

• Understand the motivation, fundamentals, and terminology of AI applications across multiple domains
• Analyze domain-specific datasets and apply suitable preprocessing and feature engineering techniques
• Implement and compare classical machine learning algorithms and deep learning architectures
• Evaluate models using appropriate metrics and error analysis for each domain
• Critically discuss the opportunities, challenges, and ethical implications of AI adoption in healthcare, social media, finance, HR, and smart farming
• Develop a project pipeline that demonstrates end-to-end application of AI to real-world problems

Requirements

• Students should have basic knowledge of machine learning concepts, programming experience in Python, and familiarity with AI libraries such as TensorFlow or PyTorch.
• Experience with cloud platforms (AWS, Azure, or GCP) and version control tools (e.g., Git) is helpful but not required.
• Teamwork and communication skills are essential for successful project completion.

General Information

• Teaching Format: Combination of pre-recorded lectures, guided demos, weekly self-tests, homework assignments, and a final project.
• Total Workload Master: 125h (40h contact / 85h self-study) / 5 ECTS
• Total Workload MBA: 100h (40h contact / 60h self-study) / 4 ECTS
• Total Workload Micro Degree: 125h (40h contact / 85h self-study) / Equivalent to 5 ECTS
• Module coordinator: Prof. Dr. Raad Bin Tareaf
• Examinations: Quizzes, presentation(s), essay(s)/paper(s), project report(s), written exam (tbd) - Details will be announced with course start.
• Offered: Even quarters