• Level of education

    two years of postsecondary education

  • ECTS

    2 credits

  • Training structure

    Faculty of Science

Description

Introduction to the use of data mining methods:

# Motivations, areas of application

# Data mining tasks (classification, estimation, prediction, etc.)

# Data processing (pre/post-processing)

# Supervised learning (Bayesian method, k-nearest neighbors, neural networks, etc.)

# Unsupervised learning (k-means)

# Evaluation of methods

# Data mining software (R, Scikit, Weka, etc.)

Read more

Mandatory prerequisites

Teaching computer science in the first and second years of CPES, and advanced study in the first semester of the second year

Read more

Knowledge assessment

Final exam

Read more