General information

The Bachelor lecture Introduction to Machine Learning is one of the compulsory modules for students of ETIT and NES.

BA ET and BA SES : Module 15

Dates and materials for lecture and exercise

Lectures and exercises are organized in their own Moodle rooms, where the semester-accompanying materials and dates are announced.
Registration takes place via LSF

Lecture content

Basic concepts of statistics
Basics of machine learning

Classification

• Naive Bayes
• Linear discriminant analysis

Regression

• Linear regression
• Nonlinear regression

Neural networks
Deep learning
Implementation of machine learning methods with the help of Matlab

Competencies

After successfully completing the module, students have basic knowledge of statistics and probability theory as well as central methods of machine learning. They will be able to identify typical problems from technical fields of application - in particular from electrical engineering, information technology and robotics - as classification or regression tasks and model them appropriately.

Students will be able to describe and differentiate between basic methods of supervised learning, such as Naive Bayes, linear discriminant analysis, regression methods and neural networks, including deep learning approaches, and evaluate their potential applications. They understand the key algorithmic principles of the training methods and can interpret and critically classify their results.

In addition, students are able to practically implement selected machine learning methods with the help of suitable software tools (e.g. MATLAB) and apply them to specific problems. Using case studies, they acquire the ability to select suitable methods and assess their performance in the application context. Overall, students are able to use basic machine learning methods in a targeted manner in the further course of their studies and in simple engineering applications.