Module Big data & Small Data

Type	Elective
Semester	summer
Contact hours	128
Number of credits	15
Type of termination	Exam
Form	Lectures + exercises, minor project
Lecturers	Carolien Kattenbelt Dr. Ir.

TARGET

Professional Task: The module covers the following tasks: Collect dataset, interpret features of dataset and create prediction model

General description: Collect a relevant dataset, Interpret the features of the dataset, Offline create prediction model using either system identification techniques or supervised machine learning and Evaluate the model.

Description of the study units:
Data Collection and Machine Learning: The students have to carry out assignments in which they learn how to clean up a dataset so that it is directly usable for prediction purposes. Furthermore students will analyze the data within the dataset and find e.g. correlation among variables.
The student also learns how to make prediction models using a large number of supervised machine learning algorithms. The student can apply these models to datasets and knows the advantages and disadvantages of the different methods.

Capita Selecta in machine learning: Critical review of literature on machine learning in a certain application area
Minor Project in Big Data: The Project serves to challenge the students to apply their knowledge and skills on a real-life problem, in correspondence with a subset of the final qualifications.
The project covers the following topics: Analyzing data of a real life problem and Developing a prediction model for this problem. The problem analysis will result in a problem solution to be translated in such terms that conclusions can be drawn based on the extent the problem has been solved and objectives fulfilled.