Modern Machine Learning in Practice

This course provides more comprehensive content on machine learning (ML) principles and techniques for developing practical ML systems. Topics covered include essential ML basics, modern prediction models, with a focus on ensemble learning, and critical steps in the ML pipeline, such as data preparation, manipulation, exploratory data analysis, data cleaning, feature engineering, and model interpretation. The course also addresses model evaluation, reproducibility, automatic ML, and specialized methods for time series data.

Knowledge: Students will understand key machine learning concepts, methods, and ethical considerations relevant to building practical applications.

Skills: Students will be able to use existing machine learning tools to preprocess data, select models, evaluate performance, and develop real-world applications.

General competence: Students will learn to collaborate in teams, apply machine learning responsibly, and adapt tools to solve practical problems effectively.

Lectures, group work, colloquia, and self-study.