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.