Machine Learning And Artificial Science
Machine learning is about developing efficient algorithms that can learn to make accurate decisions by finding patterns in past examples. There are a large number of techniques, but a few underlying principles that make machine learning work well. The applications are wide-ranging and numerous: industry, science, education, medicine, law, and arts all have many opportunities to use learning, and we work on many of them at Baylor.
Faculty Involved
Dr. Shou's research focuses on machine learning and deep learning for sequential data, particularly temporal point processes, and time series data.
Dr. Zhao's researches focus on machine learning, data mining, and artificial intelligence, particularly fairness-aware machine learning, novelty detection, and domain generalization.
Dr. Han's research spans from artificial intelligence to data science, fintech, and cybersecurity.
Dr. Gao's research lie in advanced machine learning, artificial intelligence, statistical data analysis, and computer vision to solve basic and applied biomedical problems.
Dr. Hamerly's research is focused on machine learning, covering efficient and robust clustering, detecting photographic symptoms of disease, quantification of invasive freshwater species, text mining and critical analysis, collaborative learning, and competitive programming.