Data Mining in PracticeLaajuus (3 ECTS)

Objective

By the end of the module, students should be able to:
- Develop an appreciation for what is involved in machine learning (data mining) from data
- Understand a wide variety of learning algorithms
- Understand how to evaluate models generated from data
- Apply the algorithms to solve real problems, optimize the models learned and report on the expected performance

Transferable skills:
- Mathematical analysis of learning methods.
- Evaluation of algorithms.
- Programming skills in Python

Content

This course aims to provide students with an in-depth introduction to the main topics of Machine Learning.

It will cover some of the main models and algorithms for regression, classification and clustering. Topics such as linear and logistic regression, classification trees, rules, SVMs, neural networks, clustering, feature selection and dimensionality reduction. Visualisation and evaluation of machine
learning models.

Qualifications

The course will use Python and/or R programming languages.
Some familiarity with linear algebra, probability theory.