Syllabus
Lectures:
• introduction to machine learning, motivation, examples
• general methods in machine learning: split of dataset to training and validation, over-fitting, regularization
• methods using decision trees: decision trees, random forest, gradient boosting
• methods using neural networks: simple neural networks, convolutional neural networks, recurrent neural networks
• clustering methods – supervised vs unsupervised
• other classification methods – support vector machine, naive Bayes
Practicals:
• Practicals will be held in computer lab and Python language will be used
• Machine learning algorithms will be applied on real data
Annotation
A crucial part of big data analysis is machine learning. Machine learning is widely used and is successful when solving complex tasks in many fields.
This course serves as an introduction to basic machine learning principles and its use in practice. It presents the most used methods as decision trees or neural networks, which will be implemented in practicals in Python language.
We will focus on analysis of real data and interpretation of the results.