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.)
Mandatory prerequisites
Teaching computer science in the first and second years of CPES, and advanced study in the first semester of the second year
Knowledge assessment
Final exam