Learn more about the academic programs we are delivering in Summer 2025. If you have any questions about part-time studies, please contact us.
This course introduces students to the fundamental concepts of machine learning such as supervised, unsupervised, regression analysis, classification, clustering, dimensionality reduction, and reinforcement learning as it applies to different application domains. The course will cover a range of machine learning algorithms such as decision trees, random forests, support vector machines, neural networks, and deep learning models.
The course will also focus on the practical aspects of machine learning, including data preprocessing, feature selection, model evaluation, and interpretation of results using contemporary machine learning libraries.