Machine Learning (ML)

The amount and complexity of available data is steadily increasing. To make use of this wealth of information, computing systems are needed that turn the data into knowledge. Machine learning is about developing algorithms for analysing 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 are 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 students to the basic theory and most common techniques of statistical machine learning. The students will obtain a working knowledge in statistical machine learning.

This course is relevant for computer science students as well as for students from others studies with sufficient mathematical background and programming skills (e.g., Statistics, Math-Economics, Actuarial Math, Physics, Bioinformatics, …).

The course covers the following tentative topic list:

• Foundations of statistical learning.
• Occam’s razor bound for generalisation performance.
• Vapnik-Chervonenkis (VC) analysis of generalisation performance.
• Classification methods, such as: Linear models, K-Nearest Neighbor, kernel-based methods (e.g., support vector machines), and neural networks.
• Linear and non-linear regression methods.
• Basic Clustering, dimensionality reduction and visualisation techniques, such as principal component analysis (PCA).

WARNING: This is a master-level course assuming solid math and programming skills. Please, carefully check the "Recommended Academic Qualifications" box below and the self-assessment assignment at https:/​/​sites.google.com/​diku.edu/​machine-learning-courses/​ml. It is not advised taking the course if you do not meet the academic qualifications.