We welcome all doctorate, master's, and curious 3rd-year students interested in the frontier between neurosicence and computer science. This autumm we will cover the neural basis of representation of space, and how this drives innovations in artificial intelligence.

This course aims to give students a very clear intuition behind the most essential Machine Learning methods. Therefore, we will practice implementing some of the key algorithms (like decision trees, backprop, bagging tree) in machine learning, so that it can be done if the need arises. We will discuss and gain practical experience with `sklearn` library. We will build an understanding as to when some algorithms should be preferred to other algorithms.

Containerization technologies and orchestration solutions like Kubernetes are the cornerstones of building efficient microservice-based scalable and reliable applications. This course deals with the nature, design, and development of container-based systems and cloud-native services. The knowledge acquired in the course is hardened through practical tasks.

The goals of the course are to:

The goal is to provide an understanding of high performance computing, how it works, and introduce its best practices and use cases to enable efficient calculations with large complex data.

Within this subject, you can familiarize yourself with the following topics:

• machine learning, artificial neural networks;
• image processing, object recognition;
• self-driving cars;
• natural language processing;
• business analytics;
• human-robot interaction;
• artificial intelligence in medicine and health data analysis.

This course aims to give the students a practical approach to IT systems by putting them into the role of a system administrator. During this course the student will try to shoulder different tasks a normal system administrator has to face daily, while learning about systems, integrations, monitoring and DevOps.