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Introduction to machine learning

  • Class 30
  • Practice 30
  • Independent work 120
Total 180

Course title

Introduction to machine learning

Lecture type

Elective

Course code

21-02-515

Semester

2

ECTS

6

Lecturers and associates

Course 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.

The objectives of this module are to enable students to:
• 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.

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.

Literature

Essential reading:
1. James, G., Witten, D., Hastie T., Tibshirani R. (2017) Introduction to Statistical Learning, New York: Springer-Verlag

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

Minimal learning outcomes

  • Choose one problem and describe most common algorithm to solve that problem.
  • Identify components of selected machine learning algorithms.
  • Explain process of feature reduction using machine learning algorithms.
  • Choose one problem and describe steps in solution for that problem using machine learning

Preferred learning outcomes

  • Choose the best algorithm to solve each problem.
  • Critically judge the components of selected machine learning algorithms.
  • Evaluate the impact of different feature reductions using machine learning algorithms.
  • Apply the selected machine learning method to the given problem.
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