The code here follows these seminars for ML course by K.V. Vorontsov read for MIPT students. Some tasks may change with time, so keep in mind that the code here is aimed at solving 2024/25 tasks. This repo follows the structure of 2 folders connected to 2 course semesters:
- Mathematical basics of machine learning - this is all things about
scikit-learnbasics like multiclass classifications using linear/logistic regression, Parzen window and SVM - Applied models of machine learning - more complex and applied tasks (using
pytorchand GPU acceleration) that include: CNN, LSTM, AE/VAE, image annotations, usage of fasttext, bigARTM in text date prediction
Some of the tasks here were completed collaboratively in groups or taken from other students (btw some of them decided to remain anonimous), see credits in corresponding README files.