Introduction to machine learning

Repository for Introduction to Machine Learning course (W13MST-SI2500G) - Applied Mathematics, Winter 2025/2026.

Program content The program content covers advanced knowledge in machine learning methods, their training and optimization, as well as typical application areas. Strong emphasis will be placed on implementing advanced models utilizing machine learning in the Python programming language. The course will discuss data preparation methods, key traditional machine learning algorithms for clustering, classification, and regression, as well as popular deep learning algorithms.

The full course description prepared by the university can be found here: about.pdf

Implemented algorithms

• Data preprocessing - L1: data exploration, missing values handling, categorical encoding (one-hot encoding, ordinal encoding), data scaling (standardization, min-max normalization), train-test split
• Clustering - L2: k-means, lloyd algorythm, elbow method, agglomerative clustering, silhouette score, dbscan

technologies

python, jupyter, scikit-learn, pandas, numpy, matplotlib, scipy