Registration via LPIS
Specialization in Business Administration Course IV - International Marketing Management
Specialization in Business Administration Course V - Marketing and Consumer Research
Specialization in Business Administration Course V - Retailing and Marketing
Specialization in Business Administration Course V - Service and Digital Marketing
|Tuesday||03/19/19||06:30 PM - 09:30 PM||TC.-1.61|
|Tuesday||03/26/19||06:30 PM - 09:30 PM||TC.-1.61|
|Tuesday||04/02/19||06:30 PM - 09:30 PM||TC.-1.61|
|Tuesday||04/09/19||06:30 PM - 09:30 PM||TC.-1.61|
|Tuesday||05/07/19||06:30 PM - 08:45 PM||LC.-1.038|
|Tuesday||05/21/19||06:30 PM - 09:30 PM||D2.-1.019 Workstation-Raum|
|Tuesday||05/28/19||06:30 PM - 09:30 PM||D2.-1.019 Workstation-Raum|
|Tuesday||06/04/19||01:00 PM - 03:30 PM||TC.4.02|
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 state-of-the-art 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, support vector machines (SVM), deep learning and k-means clustering.
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.
This course uses various teaching methods like classic lecture format, guided computer exercises, group works and short student presentations.
The final grade will be evaluated as follows:
- Class participation: [weight: 10%]
- Online Assessment for R (Data Camp): [weight: 10%]
- Individual computer exercises (4 homeworks): [weight: 20%]
- Final exam (open questions and multiple choice): [weight: 50%]
- Completion of the group challenge: [weight: 10%]
- Winners of the group challenge: [weight: 5%] Bonus
For the individual home exercises, a random draw will select students to make a short presentation about their findings. If a presentation has failed (no preparation) the final grade will be reduced by one grade.
To successfully pass this course, your weighted final grade needs to exceed 60%.
Basic knowledge of standard statistical software packages, such as R (or Python) are recommended. For those who are not familiar with R and to ensure a shared set of R skills among all participants, we will provide a condensed introduction to R on the first day and an online exercise.