Scalable Machine Learning

  1. Introduction into large-scale machine learning
  2. Training linear models with SGD and L-BFGS
  3. Feature interactions and hashing
  4. Online learning in Vowpal Wabbit
  5. AllReduce and distributed training in Vowpal Wabbit
  6. Feature binarization techniques for speeding up training
  7. Bin Counting and Count-min Sketch for categorical features
  8. Block-coordinate descent methods for GLMs. ADMM.
  9. GBDT scaling approaches in XGBoost and LightGBM
  10. Training Gradient Boosted Trees with LightGBM on GPU
  11. Recommender Systems: ALS, Factorization Machines
  12. Text topic modeling, LDA
  13. Clustering with distributed K-Means
  14. LSH for finding similar items
  15. Efficient structures for similar images lookup
  16. Parameter servers for distributed ML
  17. Massive time series forecast