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This lecture promotes understanding of good practices for machine learning with noisy and
inaccurate data.
In particular, we focus on feature extraction/ feature subset selection, handling high dimensional data, ANN + Deep Learning, Probabilistic graphical models, Topic models; as well as Unsupervised learning and clustering, Anomaly detection and Recommender systems.
The lecture exercises are project-based and foster hands-on experience with machine learning approaches on relevant data sets.
The lecture schedule is given below:
Attendance in to the lectures is mandatory. We will assign Machine-learning projects which are solved in teams of 3 students each. The groups will document their project progress in an academically written paper. The results achieved throughout the project are presented in the end of the course in form of a poster.
In addition, throughout the course, each group will be assigned a tutorial to present during the lecture. Furthermore, the project progress is presented to the other groups twice during the course.
The grading structure is described below. Based on the success in the respective parts of the course, students score points which then translate into the final grade. 50% of the total points are sufficient to pass the course.
