Artificial Intelligence / Machine Learning

Research Domain Artificial Intelligence / Machine Learning

The goal of a learning machine is often formalized in terms of an optimization problem, i.e., minimization or maximization of a mathematical function. Numerical methods are crucial when an analytical solution to the optimization does not exist. The most popular numerical optimization algorithms in ML use derivative information (for example, gradient descent, accelerated gradient descent methods including Momentum, Nestorov method etc., Newton's method, L-BFGS).

Application area of research group:

• Statistical and Machine Learning Techniques Applied to Algorithm Selection for Solving Sparse Linear Systems
• Machine Learning for Multi-Stage Selection of Numerical Methods
• Application of machine learning and numerical analysis to classify tremor in patients affected with essential tremor or Parkinson’s disease
• Sentiment Analysis
• Cancer Detection

Faculty Associated with the research group: