Applied Artificial Intelligence
Degree programme | Mechatronics |
Subject area | Engineering Technology |
Type of degree | Bachelor full-time |
Type of course unit (compulsory, optional) | Compulsory optional |
Course unit code | 074703055205 |
Teaching units | 30 |
Year of study | 2025 |
Name of lecturer(s) | Philipp WOHLGENANNT |
Requirements and Prerequisites
- Engineering Mathematics
- Linear Algebra
- Probability/Statistics
- Introduction to Programming
Course content
Introduction to Artificial Intelligence
- Definitions: Artificial Intelligence, Machine Learning, Deep Learning, Data Science
- History and recent developments
- Examples of applications (general and in engineering)
Introduction to classical data analysis:
- Data structures: samples, features, target
- Statistical basics: (co-)variance, correlation, statistical relationship vs. causality
- Cross-validation: training and test datasets, fitting and forecasting
- Supervised learning: regression vs. classification, k-Nearest Neighbors, linear and logistic regression, linear support vector machines, decision trees
- Unsupervised learning: clustering, scaling
Fundamentals of supervised learning using neural networks (NN)
- NN architectures, neuron models and activation functions
- Learning algorithms for feedforward networks including error backpropagation
- Generalization and the bias-variance dilemma
Deep learning architecture including Convolutional Neural Networks (CNN)
- Image classification using CNNs
Sequence models (Recurrent Neural Networks) for load forecasting
- Basic idea of LSTM
- Basic idea of Transformers
Basics of Reinforcement Learning
- Explanation of key terms such as state, action, environment, agent, or policy
- Q-learning
- Deep Learning
Learning outcomes
After successfully completing this course, students will be able to:
- Explain the basic ideas of learning algorithms
- Distinguish between correlation and causation
- Understand, apply, and interpret classical tools like k-Nearest Neighbors, clustering, and decision trees
- Explain network architectures, neuron models, and activation functions
- Describe loss/error functions and the concept of error backpropagation
- Identify application areas of different methods
- Apply standard frameworks such as PyTorch/sklearn for implementing machine learning or data science solutions
Planned learning activities and teaching methods
- Lectures
- Programming exercises, programming projects
- Coaching
Assessment methods and criteria
- Written final exam (80%)
- Exercice submissions (20%)
For a positive grade, a minimum of 50% of the possible points must be achieved across all parts of the examination.
Comment
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Recommended or required reading
- Goodfellow, I., Bengio, Y., Courville, A. (2016): Deep Learning. MIT Press. http://www.deeplearningbook.org/
- Guido, Sarah; Müller, Andreas C. (2016): Introduction to Machine Learning with Python: A Guide for Data Scientists. Sebastopol, CA: O’Reilly UK Ltd.
Mode of delivery (face-to-face, distance learning)
In-person classes. Students will be informed about attendance requirements by the lecturer before the course begins.