Curricula

Objective

Machine learning classification methods are often applied in practical problems. You will get to know the basics of machine learning, splitting data to the training and test data and evaluate the accuracy of the model. You also learn about the application of machine learning methods to various business problems.

EUR-ACE Knowledge and Understanding
You know different supervised and unsupervised learning methods.

EUR-ACE Engineering practice
You know the principles of machine learning. You know how to use different classification methods and apply them to business problems. You know how to analyze the results of machine learning methods.

Content

In this course, you will learn to apply machine learning classification methods to practical problems. You will explore the basics of machine learning, data splitting into training and test sets, and model accuracy evaluation. You will also learn to apply machine learning methods to various business problems. By the end of the course, you will be able to use different classification methods, analyze their results, and apply your knowledge in practice.

Basic principles of machine learning
Supervised and unsupervised learning methods
Division of the data set into teaching and test data
Assessment of model accuracy
Different classification methods: KNN (K-nearest Neighbor) random forest
Support Vector Machine
Principal Component Analysis (PCA)
K-Means clustering

Qualifications

Basics of Programming

Assessment criteria, satisfactory (1)

Sufficient (1)
You know the main principles of machine learning. You recognize some learning methods. You know how to use some classification methods. You know how to produce results from machine learning methods.

Satisfactory (2)
You know the main principles of machine learning. You recognize different learning methods. You know how to use different classification methods. You can interpret the most common results of machine learning methods.

Assessment criteria, good (3)

Good (3)
You know the principles of machine learning. You recognize supervised and unsupervised learning methods. You know how to apply different classification methods. You know how to use the results of machine learning methods.

Very good (4)
You know the principles of machine learning. You can distinguish between different supervised and unsupervised learning methods. You can apply classification methods to a given business problem and compare the results obtained. You know how to analyze the results of machine learning methods.

Assessment criteria, excellent (5)

Excellent (5)
You can explain the principles of machine learning. You can recommend the use of various supervised and unsupervised learning methods for a specific business problem. You can apply classification methods to various business problems and interpret the results obtained. You can analyze and justify the results given by machine learning methods.