Introduction to machine learning

Module title:

Introduction to machine learning

Module overview:

Machine learning forms the foundation of today´s data science. Data processing by machine learning methods results in a predictive model, but applications are far wider than the prediction itself, so machine learning is used for any input and output mapping that is too hard to manually input or for which there are no clearly defined rules to be entered, or these rules change too often. Machine Learning is divided into supervised, uncontrolled and awarded. This module will deal primarily with supervised machine learning, although the part will be dedicated to uncontrolled. Awarded learning is part of advanced topics, and this topic will be discussed in other modules. Students learn to identify and understand basic algorithms for automatic data processing.
It is important for students to take this module in order to adopt basic machine learning algorithms and basic techniques of their optimization, as well as the methods of reduction of features, needed for other modules in this study programme.

In this module students will learn:
Evaluate the strengths and weaknesses of machine learning algorithms
Appraise the suitability of a machine learning algorithm to solve a given problem
Formulate appropriate methodologies to evaluate the accuracy and robustness of machine learning algorithms.
Implement machine learning algorithms to solve classification and regression problems.
Develop predictive models with machine learning algorithms.
Design unsupervised clustering programs based on machine learning algorithms.

Literature:

Required readings:
1. James, G., Witten, D., Hastie T., Tibshirani R. (2017) Introduction to Statistical Learning, New York: Springer-Verlag
2. Thakur, A. (2020). Approaching (almost) any machine learning problem.

Supplementary readings:
1. Géron, A. (2019) Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, Massachusetts: O’Reilly