M2-1: AI and Machine Learning

Full course description

Elective Module Project 2-1 > M2-1: Artificial Intelligence and Machine Learning

Course: Machine Learning (period 2) +
Project: Project 2-1 Adaptive Systems (semester 3, bachelor year 2)

Each elective module comprises an elective course, an elective project, and related skill classes.

Second year students choose one out of the two electives (Module Project 2-1) during semester 3.

• Course Machine Learning (period 2): Machine learning is a major frontier field of artificial intelligence. It deals with developing computer systems that autonomously analyse data and automatically improve their performance with experience. This course presents basic and state-of-the-art techniques of machine learning. Presented techniques for automatic data classification, data clustering, data prediction, and learning include Decision Trees, Bayesian Learning, Linear and Logistic Regression, Recommender Systems, Artificial Neural Networks, Support Vector Machines, Instance-based Learning, Rule Induction, Clustering, and Reinforcement Learning. Lectures and practical assignments emphasize the practical use of the presented techniques and prepare students for developing real-world machine-learning applications.
• Project 2-1 Adaptive systems: In this project, you will use Artificial Intelligence (AI) and Machine Learning (ML) techniques to control autonomous agents in a real-time video game engine. There will be a strong emphasis on combining and using existing libraries and tools (e.g., a video game engine, and existing implementations of AI and ML techniques), and building your own new code around it. You will use GitHub to host your code and effectively collaborate with your teammates, include clear documentation (for yourselves and others) on how to correctly install any dependencies, and end up with a project that you can proudly display on your GitHub profile as part of your portfolio of programming work. The used programming languages will be C# and Python.