Predictive Analytics and Machine Learning
This course teaches students how to use machine learning techniques and tools for predictive analytics. The course will also teach students to: specify problems accessible with these methods, evaluate analytics models, use new tools in the rapidly evolving automated machine learning field, and deploy machine learning models. The course assumes prior experience with: basic probability theory, traditional methods for analysis of continuous and categorical variables, and introductory Python programming.
This course focuses on predictive analytics and machine learning applications: supervised learning (classification and regression). The course will also introduce unsupervised learning, including clustering, market basket analysis, and dimensionality reduction. Students will implement and tune individual machine learning algorithms (e.g., via Python’s scikit-learn library), and work with tools in the rapidly evolving field of automated machine learning. In addition to lecture, the course will include cases, in-class workshops, and guest speakers as appropriate.
This is an excerpt from the complete course description for the course. If you are an active student at BI, you can find the complete course descriptions with information on eg. learning goals, learning process, curriculum and exam at portal.bi.no. We reserve the right to make changes to this description.