The course provides an overview of machine learning (ML) methods and algorithms for data science. It consists of two parts:

The first part of the course discusses the emergence of big data, presents common ML-based visualization techniques for large datasets, and introduces basic ML methods such as principal component analysis, k-means clustering, linear regression, and linear discriminant.

The second part of the course introduces advanced ML techniques (probabilistic models, kernels, and deep learning) which address the uncertainty of the model and the nonlinearity of real-world data. It also covers the question of reproducibility and explainability of ML models.

By the end of the course, the student has become familiar with common ML models for data science, has understood their theoretical and algorithmic foundations, and has gained experience on how to use these models adequately in practical situations.

List of topics:

- Big data and data science

- ML-based data visualization

- Principal components, anomalies, and clustering

- Correlation analysis, linear regression, and linear discriminant

- Probabilistic models

- Kernel methods

- Deep learning

- Reproducibility

- Explainable AI