In finance, marketing, consulting, public policy, science, you name it, people who can make sense of large and complex data sets are in high demand. Machine learning refers to a vast set of tools that help us in making sense of data. This course gives an introduction to various machine learning methods, concentrating on those that fall under the umbrella of supervised learning. The course contains both model based and algorithm based machine learning approaches, contrasting the advantages and limitation of the two. While the course contains some mathematical statistics meant to enable the students to peek into various algorithms, the focus is on doing things. The thing we do is to make sense of big data sets, and in so doing we will use the programming language R.
- Introduction to R.
- What is machine learning.
- Linear regression, including dimension reduction methods.
- Classification. Logistic regression, linear discriminant analysis, naive Bayes, and K nearest neighbours.
- Bootstrapping and other resampling methods.
- Tree-based methods. Bagging, random forests, boosting.
- Data cleaning.
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.