Curricula

Objectives

The student understands the significance of machine learning in digitalizing operational environment. The student knows the most common machine learning methods, is able to apply them in practice to existing data and interpret the results of the methods. In addition, the student understands the mathematic behind the most commonly used machine learning algorithms.

Course competences
EUYEE EUR-ACE: Engineering Design, Master's Degree
EUYIV EUR-ACE: Investigations, Master's Degree
EUYER EUR-ACE: Engineering Practice, Master's Degree

Content

- Mathematics behind the machine learning algorithms
- Most common regression and classification models of supervised machine learning and their application in Python programming environment using NumPy, Pandas and Scikit-learn libraries.
- Choosing a suitable machine learning algorithm model and estimation of results
- Support Vector Machine
- Clustering
- K-nearest neighbor
- Reinforcement learning

Learning materials and recommended literature

Lecture notes, exercises, video examples
Muller, Guido: Introduction to Machine Learning with Python

Teaching methods

Virtual study, contact study.

Student workload

Virtual study 110 h
Contact study 15 h

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

Sufficient 1: Student has sufficient knowledge of machine learning algorithms and neural networks. Student is able to apply the most common techniques of machine learning and has the sufficient knowledge of the mathematics behind the techniques. Additionally, the student is able to estimate and validate implementation briefly.

Satisfactory 2: Student has satisfactory knowledge of machine learning and neural networks. Student is able to choose a suitable machine learning technique and apply the technical know-how in practice. Student understands the mathematics behind the techniques at a satisfactory level. Additionally, the student is able to estimate and validate implementation superficially.

Evaluation criteria, good (3-4)

Good 3: Student has good knowledge of machine learning algorithms and neural networks. Student is able to choose an appropriate machine learning technique and apply the technical know-how in practice. Student understands well the mathematics behind the techniques at a good level. Additionally, the student is able to estimate and validate implementation well.

Very good 4: Student has very good knowledge of machine learning algorithms and neural networks. Student is able to choose and justify versatilely a machine learning technique and apply the technical know-how in practice. Student understands the mathematics behind the techniques at a very good level. Additionally, the student is able to estimate and validate implementation critically.

Evaluation criteria, excellent (5)

Excellent 5: Student has excellent knowledge of machine learning algorithms and neural networks. Student is able to choose and justify machine learning technique and apply the technical know-how in practice. Student understands the mathematics behind the techniques at an excellent level. Additionally, the student is able to estimate and validate implementation critically.