An increasingly complex global business environment requires firms’ to make use of the large amounts of data out there in order to make better decisions. Machine learning allows to “automatically detect patterns in data, and then use the uncovered patterns to predict future data” (K. Murphy, Research Scientist at Google). Using data analytics techniques, large data and detected patterns can be visualized and communicated to decision makers. Both, rapidly evolve and change businesses as well as companies’ financial processes. This opens up the possibility of using machine learning approaches to cope with complicated real-world financial problems that are too complex for humans.
Goals of the course
As a result of participating in this course, a student is expected to
- understand the goals and capabilities of machine learning,
- apply machine learning approaches to real-world financial problems,
- use important data analytics methods to evaluate large data sets,
- using an approachable, and well-known programming language, Python.
- Content: Focus on unsupervised and supervised machine learning,
- Software/Programming language: Introduction to programming language Python,
- Application: Clustering and Prediction, Data Analytics and Visualization in Python.
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