The fast rise of available data for customer and market analysis creates an immense opportunity for machine learning applications. For predictive tasks, these models often outperform conventional statistical approaches. This course introduces some of the modern machine learning techniques including the basic theoretical background with hands-on exercises and group works.

The course includes a short introduction to R, supervised machine learning, model performance evaluation, cross-validation and generalization. We will focus on the application and the concept of the following machine learning methods: k-nearest neighbors (kNN), decision trees, random forests, k-means clustering, neural networks and support vector machines (SVM).

The learning objectives of this course are as follows:

• Understand the basic theoretical background of various supervised machine learning algorithms.
• Get familiar with various machine learning algorithms in R by exercising on real data examples.
• Gain the knowledge to pick the right machine learning method for a particular problem.

The final grade will be evaluated as follows:

• Class participation: [weight: 10%]
• Individual computer exercises (5 homeworks): [weight: 50%]
• Final exam (open questions and multiple choice): [weight: 40%]
• Group challenge: [weight: 10% Bonus in Total]

For the individual home exercises, a random draw will select students to make a short presentation about their findings. If a presentation has failed, the final grade will be reduced by one grade.

To pass this course, your weighted final grade needs to exceed 60%.