Machine Learning can help to identify patterns in complex data sets, understand factors impacting a specific outcome, and increase the quality of business decisions through more accurate predictions. This course focuses on supervised machine learning techniques for the analysis and evaluation of data. Besides covering different types of statistical machine learning models needed for complex business analytics problems, it also considers aspects of data preparation, model development, training, evaluation, and model parameter optimization.

On successful completion of the course, you should be able to:

• Demonstrate in-depth knowledge of supervised machine learning models, specifically for Regression and Classification
• Train, evaluate, and apply supervised learning models on business analytics problems
• Correctly interpret results of supervised machine learning models and make appropriate (business) recommendations
• Demonstrate an understanding of the requirements and assumptions of the various models
• Implement techniques for data preparation, model selection, and model performance improvements
• Demonstrate knowledge of the important parameters of the considered methods and an ability to optimize the parameters for a given dataset
• Independently conduct a machine learning project using a state-of-the-art statistical tool

In order to successfully pass this course, your absence is limited to 20 % of our appointments.

The course will cover the entire lifecycle of a machine learning project, with specific emphasis on the training, evaluation, and optimization of different types of supervised machine learning models. It will use a combination of lectures and hands-on implementation sessions to introduce, analyze, and evaluate different machine learning concepts. As part of the course, students will also work on a semester-long course project in which they apply the various machine learning techniques learned in class.

The assessment of the course is based on following components:

• 40%: Homework assignments, focusing on specific topics of the course
• 40%: Implementation of a machine learning project, including a written final report
• 20%: Presentation of the course project

Grading scheme

• Excellent ≥87,5%
• Good ≥75,0%
• Satisfactory ≥62,5%
• Sufficient ≥50,0%
• Fail <50,0%

The course builds on prior statistics/mathematics courses and targets students with a Data Analytics focus who want to learn more about machine learning techniques. Prerequisites are:

• Prior statistics / mathematics courses covering basic statistical methods, particularly (linear) regression
• Willingness to learn a state-of-the-art tool for machine learning (prior programming/scripting experience is a plus, but not required)

Christian Haas