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Machine learning research at CCIS builds on a wide variety of techniques that enable the conversion of data into knowledge—empowering intelligent computer systems to solve tasks by automatically learning from data and without being explicitly programmed. Our research runs the gamut from theory and algorithm creation to implementation and end-use applications. Given the vast amounts of data being collected about all aspects of modern life, machine learning techniques are critical to utilizing and analyzing the data in ways that strengthen these intelligent systems and make them evermore autonomous, powerful, and impactful.

Areas of investigation:

  • Machine learning theory
  • Deep learning
  • Graphical models
  • Learning-to-rank
  • Semi-supervised learning

Machine learning has found widespread applications in almost all aspects of 21st century life. Machine learning algorithms are commonly used in health informatics, computer security, recommendation systems, fraud detection, speech recognition, computer vision, search engines, economics, self-driving vehicles, and social networks. At CCIS, our renowned group of research faculty is developing new algorithms and real-world techniques that are pushing the boundaries of supervised, semi-supervised, and unsupervised learning. PhD and Masters students work in both disciplinary and interdisciplinary groups, investigating new methodologies for applying machine learning to areas such as personal health informatics, computer security, social networks, computer vision, robotics, natural language understanding, and information retrieval.

Research Area - Machine Learning