(Fall semester, lecture, Block course)
Machine Learning has become one of the core pillars of information technology. Since the amount of available data is steadily increasing, smart data analysis will become more and more important in the future. This course introduces Machine Learning in a non-technical, hands-on way with integrated exercises and group works.
A definition of Machine Learning, sampling and cross-validation, performance evaluation, logistic regression, decision trees, random forest, deep learning, and ensemble methods are among the topics to be discussed in this course.
The learning objectives of this course are as follows:
- Get familiar with the concept of machine learning.
- Understand the basic theory behind various machine learning techniques.
- Apply different machine learning techniques and interpret the results.
Dr. Markus Meierer
MA students, assigned to “Wahlpflichtbereich BWL 4”
Each Fall Semester
Hastie, T., Tibshirani R., Friedman, J. (2013): The Elements of Statistical Learning – Data Mining, Inference, and Prediction, 2nd edition, Springer.
Recommended: Marketing Analytics I, A non-technical introduction to R
Individual evaluation based on contribution in class, multiple-choice tests and exercises
Dates and Location:
Block Course: 9.9.19 - 13.9.19; 9h - 17h
Location: AND 4.06
Don’t forget to officially register yourself using the registration tools at the University of Zurich.
The information in the website or syllabus supports the official information in the electronic university calendar (VVZ – Vorlesungsverzeichnis). In case of doubt, the official information at the VVZ is valid.