Introduction to Machine Learning
The course covers the main tasks of teaching by use cases: classification, clustering, regression, dimension reduction. The methods of their solution, both classical and new, created over the past 10-15 years, are being studied. The emphasis is on a deep understanding of the mathematical foundations, relationships, advantages and limitations of the methods under consideration. Theorems are mostly given without proofs.
- Linear classifier and stochastic gradient.
- Metric methods of classification and regression.
- Support vector machine.
- Linear regression. Nonlinear regression.
- Model selection criteria and feature selection methods.
- Logical classification methods.
- Linear ensembles. Advanced methods of ensembling.
- Density estimation and Bayesian classification.
- Clustering and partial learning.
- Deep neural networks.
- Deep neural networks for unsupervised learning.
- Vector representations of texts and graphs.
- Attention models and transformers.
- Topic modeling.
- Learning to rank.
- Recommendation systems.
- Search for associative rules.
- Adaptive forecasting methods.
- Incremental and online learning.
- Reinforcement learning.
- Active learning.
At the end of the course, students take an oral exam on all topics of the course.
Probability theory, statistics, optimization methods, linear algebra.