Course content
Machine learning has gained widespread recognition for its significant contributions to various facets of business operations, including tasks such as fraud detection, sales forecasting, inventory pricing, and consumer segmentation. This course is tailored to introduce business students to the world of machine learning. It places a strong emphasis on predictive analytics, empowering students to address business challenges using data-driven machine learning algorithms. The course is comprehensive, offering a balanced blend of theory and practical application. It covers essential mathematical and statistical concepts and guides students in mastering Python programming from scratch. Each class comprises both theoretical lectures and practical workshops. To ensure active participation, students are required to bring their own laptops to class.
Preliminary assignment: Several questions and tasks related to mathematical fundamentals and the installation of Python.
Class 1: Introduction and getting started with Python
Class 2: Data manipulation using Python
Class 3: Data visualization in Python
Class 4: Linear regression
Class 5: Logistic regression
Feedback activity: A small assignment (with several questions)
Class 6: Neural networks
Class 7: K-nearest neighbors and naive Bayes
Class 8: Tree-based methods
Class 9: Support-vector machines
Class 10: Cluster analysis
See course description in course catalogue