Linear Algebra and Optimization for Machine Learning

State-of-the-art machine learning methods combine techniques from many different areas of mathematics. In this course, we will discuss algorithmic foundations from the areas of linear algebra and optimization. This also includes aspects of their theory and efficient implementation. Among others, we will study

• matrix decomposition and factorization techniques,

• regression and classification problems and regularization,

• iterative and continuous optimization methods,

• hyper parameter optimization,

• graph-based algorithms and clustering,

• basic introduction to neural networks and algorithmic aspects neural networks (e.g. computational graphs and back-propagation).