Study program: F3 Computer Science

Degree: Bachelor

Type of module: lectures, practical work

Lecturer: Professor, Doctor of Science Iryna Perova

Language: English

Credit Points: 5 ECTS

Description:
Topics covered (lectures and practical work)
Topic 1. Artificial intelligence, intelligent systems, machine learning. Basic concepts;
Topic 2. Anaconda Navigator, Python, introduction, modules;
Topic 3. Python programming (simple data structures, DataFrames);
Topic 4. Data preprocessing (standardization, outlier detection, fill in gaps etc.) (+practical work);
Topic 5. Feature selection. Feature extraction;
Topic 6. Data visualization (PCA, TSNE) (+practical work);
Topic 7. Exploratory data analysis (EDA) (+practical work);
Topic 8. Clustering (k-means, DBSCAN, hierarchical clustering etc). Metrics for clustering assessment (+practical work);
Topic 9. Classification tasks (KNN, SVM, Naïve Bayes) (+practical work);
Topic 10. Classification tasks (decision trees, Random forest model etc.). Metrics for classification tasks assessment (+practical work).
Topic 11. Regression analysis (linear regression, logistic regression). Metrics (+practical work). 
Topic 12. Practical examples of ML implementation.