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