- Main types of algorithms in Machine Learning, specifics of their application
- Decision trees, Gradient boosting
- Neural networks: introduction
- Recommender systems
- Final course project description
- Final course project: requirements
- Decision trees in detail
- Decision tree ensembles
- XGBoost in detail
- LightGBM in details
- CatBoost in details
- XGBoost vs LightGBM vs CatBoost
- Ensemble methods: efficient hyperparameter tuning
- Ensembles of different models
- Natural language processing: introduction
- Word Embeddings
- Language Model
- RNN
- LSTM
- GRU
- Pytorch framework
- Latent semantic analysis
- Latent Dirichlet Allocation
- Seqence2sequence
- Attention mechanism
- Dialog systems
- BERT model for NER task
- Conditional random field
- Time series analysis
- Recommender systems
- Candidate generation methods
- Scoring
- Ranking
- Clustering algorithms: k-means, EM algorithm
- Dimensionality reduction: PCA, t-SNE, Autoencoder