The Statistics group at Johns Hopkins University’s Applied Mathematics and Statistics Department is home to a happy and friendly crew of probabilists, statisticians, harmonic analysts, optimizers, graph theorists, and machine learning experts. We develop novel, theoretically sound, and robust methodologies for the analysis of complex, high-dimensional data, while also proving theorems, writing code, developing cutting-edge undergraduate and graduate courses, drinking coffee, playing board games, asking lots of questions at seminars, and bugging domain experts to tell us more about their data. Our experts have revolutionised image analysis mathematical and statistical theory, enhanced the delivery of medical interventions, and developed systematic ways to the analysis of time-varying networks. Our team includes IMS Fellows, National Academy of Sciences members, an Egan Balas Prize winner, and at least two honorary and well-loved canines. Are you interested in statistics? You are now a member of our club.

Machine Learning

Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data.

Related Courses

Complete descriptions appear in the course catalog

View the semester course schedule.   

  • EN.553.767: Iterative Algorithms in Machine Learning: Theory and Applications 
  • EN.553.669: Large-Scale Optimization for Data Science
  • EN.553.432/632: Bayesian Statistics
  • EN.553.733: Nonparametric Bayesian Statistics
  • EN.553.743: Equivariant Machine Learning
  • EN.553.413/613: Applied Statistics and Data Analysis
  • EN.553.450/650: Computational Molecular Medicine
  • EN.553.742: Statistical Inference on Random Graphs
  • EN.553.739: Statistical Pattern Recognition
  • EN.553.688: Computing for Applied Mathematics
  • EN.553.740: Maching Learning I
  • EN.553.741: Machine Learning II
  • EN.553.74*: Mathematical Foundations of Computational Anatomy
  • EN.553.730: Statistical Theory I
  • EN.553.731: Statistical Theory II
  • EN.553.720: Probability Theory I
  • EN.5530.721: Probability Theory II

Learn More

Research and academic opportunities in statistics and machine learning