Sberbank Advanced ML and DL Course

Week 1

1. Main types of algorithms in Machine Learning, specifics of their application
2. Decision trees, Gradient boosting
3. Neural networks: introduction
4. Recommender systems
5. Final course project description

Week 2

1. Final course project: requirements
2. Decision trees in detail
3. Decision tree ensembles
4. XGBoost in detail
5. LightGBM in details
6. CatBoost in details
7. XGBoost vs LightGBM vs CatBoost
8. Ensemble methods: efficient hyperparameter tuning

Week 3

1. Ensembles of different models
2. Natural language processing: introduction
3. Word Embeddings
4. Language Model
5. RNN
6. LSTM
7. GRU
8. Pytorch framework

Week 4

1. Latent semantic analysis
2. Latent Dirichlet Allocation
3. Seqence2sequence
4. Attention mechanism
5. Dialog systems
6. BERT model for NER task

Week 5

1. Conditional random field
2. Time series analysis
3. Recommender systems
4. Candidate generation methods
5. Scoring
6. Ranking
7. Clustering algorithms: k-means, EM algorithm
8. Dimensionality reduction: PCA, t-SNE, Autoencoder