Teaching

2024 Fall

The course provides a fundamental understanding of the concepts and principles of machine learning techniques. For this purpose, the class covers an introduction to the development of machine learning in the area of AI-enabled systems. The main topics include supervised and unsupervised learning approaches as well as reinforcement learning. State-of-the-art machine learning techniques such as support vector machines and neural networks are introduced and analyzed. Throughout the semester, students will work in groups on a ML project that they will present at the end of the course. The goal of the course is to provide a holistic view that enables the students to design and train ML algorithms for a wide variety of problems.

Materials (Requires Vanderbilt credentials)

2024 Spring

The course provides a basic understanding of how to make use of machine learning techniques for dynamical system. For this purpose, the class covers an introduction to dynamical system theory and how to model and control systems on the basis of observed data. The main topics for dynamical systems include the representation of nonlinear systems, stability analysis, basic control concepts and identification methods. The main machine learning approaches that will be discussed are supervised learning techniques with focus on neural networks, bayesian approaches and physics-informed learning. Typical applications of the approaches presented in this course focus on mechanical and robotic systems.

Materials (Requires Vanderbilt credentials)

2023 Fall

Theoretical and algorithmic foundations of supervised learning, unsupervised learning, and reinforcement learning. Linear and nonlinear regression, kernel methods, support vector machines, neural networks and deep learning methods, instance-based methods, ensemble classifiers, clustering and dimensionality reduction, value and policy iteration. Explainable AI, ethics, and data privacy.

Materials (Requires Vanderbilt credentials)

2023 Spring

The course provides a basic understanding of how to make use of machine learning techniques for dynamical system. For this purpose, the class covers an introduction to dynamical system theory and how to model and control systems on the basis of observed data. The main topics for dynamical systems include the representation of nonlinear systems, stability analysis, basic control concepts and identification methods. The main machine learning approaches that will be discussed are supervised learning techniques with focus on neural networks, bayesian approaches and physics-informed learning. Typical applications of the approaches presented in this course focus on mechanical and robotic systems.

Materials (Requires Vanderbilt credentials)