Contents

General Information

This course is taught by Franz Pernkopf and Christian Knoll.

Aims and objectives of the course: Knowledge of the most important concepts and methods form the areas machine learning, neural networks, statistical modelling and classification.

Covered Topics:

• Introduction to Machine Learning
• Simple Classification and Regression Algorithms
• Learning Algorithms for Neural Networks
• Practical Classification Algorithms
• Unsupervised Learning
• Hidden Markov Models
• Graphical Models

Prerequisites

There are no particular courses which must be taken as prerequisites for this course. Although there will be an introduction to Python in the beginning of the exercises, it is recommended to have already some basic knowledge and experience in it. We also assume elementary mathematical knowledge in probability theory, statistics, analysis and calculus.

Exam for the lecture course

Six exam dates during the academic year are offered. The exam consists of open questions on all topics covered in the lecture.

Lecture Material

All the lecture material is provided in the TeachCenter