This course is the substitute for the Core Course "Theory and Practice of Machine Learning" that cannot be offered this semester.
The module is concerned with theories, concepts, and practices to inform and support managerial decision making by means of formal, data oriented methods. Students have the opportunity to develop a variety of skills, including:
Topics & Content:
Fundamentals of Business Analytics
Making data accessible: Tools for summarization, grouping, and visualization
The business case for predictive modeling
Prediction methods for regression and classification
Advanced data types: time series, text, survival, and network data Fundamentals of intelligent search
Further elaboration of lecturing material
Practical PC exercises
The lecture is accompanied by a tutorial session, in which lecture topics are further elaborated. The aim of the tutorial is to develop and assess empirical models using contemporary data science software. More specifically, the Python programming language is used in tutorial session. Students who are not familiar with Python are given an opportunity to learn Python/programming fundamentals in the first weeks of the tutorial sessions. In order to acquire the skills needed for the course in such short time frame, students must be prepared to invest ample time into self-study exercises.
Time & venue:
Lectures: Thursdays, 10:00-12:00; HU Berlin, Spandauer Str. 1, room 202
Tutorials: Thursdays, 14:00-16:00; HU Berlin, Spandauer Str. 1, room 125 or Fridays, 16:00-18:00; HU Berlin, Spandauer Str. 1, room 220
More information can be found on Moodle.