Инженерия искусственного интеллекта (09.04.01)

The curriculum of the course contains a following topics:

Introduction to Classical Machine Learning
History and Concepts
Data Preprocessing Methods
Data Cleaning
Data Normalization
Data Standardization
Regression
Linear Regression (Simple, Multiple, Polynomial)
Support Vector Regression
Decision Tree Regression
Random Forest Regression
Classification
Logistic Regression
K-Nearest Neighbours (K-NN)
Support Vector Machine (SVM)
Kernel SVM
Naive Bayes
Decision Tree Classification
Random Forest Classification
Clustering
K-Means Clustering
Hierarchical Clustering
Association Rule Learning
Reinforcement Learning
Feature Engineering
Dimensionality Reduction
Principal Component Analysis (PCA)
Linear Discriminant Analysis (LDA)
Model Selection and Boosting
Gradient Boosting
XGBoost
The Discipline of Classical Machine Learning Methods covers lectures, workshops, control tests, and exam. The final grade will be combination of the performance score from these subcategories.