Applications of AI
Micro-DegreeOnline

Applications of AI

Book NowGet in Touch

Program Details

  • Language: English
  • Fees: €900
  • Study Mode: Full-time / Part-time
  • Registration Deadline: Q4: September 30th, 2026
  • Entry requirements:
    • High school diploma or equivalent
    • Basic computer literacy
    • English Level B1 (CEFR) or equivalent

Study Access

Pay for one quarter and have access to the learning materials for 6 months, with the option to extend access if needed.

About This Course

Artificial Intelligence is driving innovation across industries by enabling the analysis of large-scale datasets, the automation of decision processes, and the generation of actionable insights. In this module, we will study both the conceptual foundations and the practical implementations of AI. Using publicly available big data sets from each of the five domains, you will learn how to:

  • Clean and preprocess data
  • Explore and engineer features
  • Train and compare models using both classical machine learning and deep neural networks
  • Evaluate AI pipelines in terms of performance, fairness, interpretability, and domain-specific challenges

Through this combination of theory and hands-on work, you will acquire a comprehensive understanding of AI applications in diverse real-world contexts.

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

The module assumes solid Python skills for data preprocessing and visualization, and familiarity with classical machine learning.

The following university sources are recommended:

Harvard IACS CS109A — Pandas, EDA & modeling (lecture notes & notebooks)

Stanford CME 193 (Scientific Python) — syllabus & lecture slides (NumPy, pandas, matplotlib, scikit-learn)

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
    • MBA: 100h (40h contact / 60h self-study) / 4 ECTS
    • Micro Degree: 125h (40h contact / 85h self-study) / Equivalent to 5 ECTS

Frequently Asked Questions