Introduction to Data Science (IDS)

The amount and complexity of available data are steadily increasing. To make use of this wealth of information, computing systems are needed that turn data into knowledge. Machine learning is about developing the required software that automatically analyses data for making predictions, categorisations, and recommendations. Machine learning algorithms are already an integral part of today's computing systems - for example in search engines, recommender systems, or biometrical applications. Machine learning provides a set of tools that is widely applicable for data analysis within a diverse set of problem domains such as data mining, search engines, digital image and signal analysis, natural language modelling, bioinformatics, physics, economics, biology, etc.

The purpose of the course is to introduce non-Computer Science students to probabilistic data modelling and the most common techniques from statistical machine learning and data mining. The students will obtain a working knowledge of basic data modelling and data analysis using fundamental machine learning techniques.

This course is relevant for students from, among others, the studies of Cognition and IT, Bioinformatics, Physics, Biology, Chemistry, Economics, and Psychology. 

The course covers the following tentative topic list:

• Foundations of statistical learning, probability theory.
• Classification methods, such as Linear models, K-Nearest Neighbor.
• Regression methods, such as Linear regression.
• Bayesian Statistics
• Clustering.
• Dimensionality reduction and visualisation techniques such as principal component analysis (PCA).