Undergraduate Programs

Biomedical Data Science for Undergraduates

Graduate Programs

Biomedical Data Science (full-time: residential)

Data Science Master’s Program (full-time: residential)

Data Science Full-time Online Master’s Program (full-time: hybrid, online)

Engineering for Professionals: Artificial Intelligence Programs (part-time: online)




The courses listed below are a sampling of offerings at Johns Hopkins University.

Core Machine Learning
BioStats 644: Statistical Machine Learning: Methods, Theory, and Applications: Vadim Zipunnikov
BioStats 646-649: Essentials of Probability and Statistical Inference I-IV: Michael Rosenblum
BioStats 776: Statistical Computing: Hongkai Ji
CS 475/675: Machine Learning: Mark Dredze
CogSci 371/671: Bayesian Inference: Colin Wilson
CogSci 371/671: Formal Methods in Cognitive Science: Inference Paul Smolensky
CogSci 372/672: Formal Methods in Cognitive Science: Neural Networks Paul Smolensky
ECE 412/612: Machine Learning for Signal Processing: Najim Dehak
ECE 447: Introduction to Information Theory and Coding: Sanjeev Khudanpur

AMS 743: Graphical Models: Laurent Younes
AMS 835: Topics in Statistical Pattern Recognition: Carey Priebe
BioStats 751-755: Advanced Methods in Biostatistics I-IV: Brian Caffo; Thomas Louis; Jeffrey Leek; Ciprian Crainiceanu
BioStats 763: Bayesian Methods: Gary Rosner
CS 476/676: Machine Learning: Data to Models: Suchi Saria
CS 477/677: Causal Inference: Ilya Shpitser
CS 479/679: Representation Learning: Raman Arora
CS 775: Statistical Machine Learning: Raman Arora
CS 792: Unsupervised Learning: From Big Data to Low-Dimensional Representations: Rene Vidal
CS 875: Selected Topics in Machine Learning: Mark Dredze, Suchi Saria, Jason Eisner, Raman Arora
Math 795: Seminar in Data Analysis Mauro Maggioni; James Murphy

Mathematical, Statistical, and Computational Background
AMS 420/620: Introduction to Probability: John Wierman
AMS 430/630: Introduction to Statistics: Avanti Athreya
AMS 433/633: Monte Carlo Methods: James Spall
AMS 692: Matrix Analysis and Linear Algebra: Youngmi Hur
AMS 723: Markov Chains: Jim Fill
AMS 730: Statistical Theory: Carey Priebe
AMS 732: Bayesian Statistics: Yanxun Xu
AMS 737: Distribution-Free Statistics and Resampling Methods: Laurent Younes
AMS 761: Nonlinear Optimization I: Daniel Robinson
AMS 762: Nonlinear Optimization II: Daniel Robinson
AMS 763: Stochastic Search and Optimization: James Spall
AMS 831: Advanced Topics in Bayesian Statistics: Yanxun Xu
BME 616: Introduction to Linear Dynamical Systems: Sridevi Sarma
BioStats 771: Advanced Statistical Theory I: Daniel Sharfstein
BioStats 772: Advanced Statistical Theory II: Daniel Sharfstein
CS 320/620: Parallel Programming: Randal Burns
CS 325/425: Declarative Methods: Jason Eisner
CS 464/664: Randomized Algorithms: Rao Kosaraju
ECE 651: Random Signal Analysis: Archana Venkataraman
Beyond these basic courses, JHU offers many relevant advanced courses on prob/stats theory, Markov chains, FFT and wavelet transforms, stochastic processes, discrete and continuous optimization, streaming and parallel algorithms, etc.


Applied Machine Learning
AMS 450: Computational Molecular Medicine: Donald Geman
AMS 735: Topics in Bioinformatics: Donald Geman
BME 487: Foundations of Computational Biology I: P Fleming
BME 488/688: Foundations of Computational Biology & Bioinformatics II: Rachel Karchin
BioStats 638: Analysis of Biological Sequences: Sarah Wheelan
BioStats 656: Multilevel Statistical Models in Public Health: Elizabeth Colantuoni
BioStats 688: Statistics for Genomics: Jeffrey Leek; Rafael Irizarry
BioStats 698: Bioperl: J Anderson
BioStats 841: Protein Bioinformatics: F Lebeda; M Olson
ChemBE 414/614: Protein Structure Prediction and Design: Jeffrey Gray
ECE 610: Computational Genomics: M Ermolaeva
Courses on machine learning for biology span the Biostatistics and Bioinformatics programs. Biostatistics is in the Bloomberg School of Public Health, and Bioinformatics is a joint offering of the Zanvyl Krieger School of Arts and Sciences and the Whiting School of Engineering. Besides the ML-oriented courses listed above, there exist many courses for different areas of specialization.

CS 336: Algorithms for Sensor-Based Robotics: Gregory Hager
CS 445-446: Computer Integrated Surgery I-II: Russ Taylor
CS 745: Seminar on Computer Integrated Surgery: Peter Kazanzides
MechE 646: Introduction to Robotics: Louis Whitcomb
MechE 647: Adaptive Systems: Louis Whitcomb
Robotics research at JHU spans many laboratories, including the Computational Sensing and Robotics Laboratory, the Locomotion in Mechanical and Biological Systems, and the Computer Integrated Interventional Systems Lab. There are many more domain courses listed at the Computer Integrated Surgical Systems and Technology page.

Econ 611: Decision Theory: Edi Karni
Econ 614: Mathematical Economics: Ali Khan
Econ 615: Mathematical Methods in Economics I: Edi Karni
Econ 618: Game Theory: Hülya Eraslan
Econ 633: Econometrics: Yingyao Hu

Language and Speech
CS 465/665: Natural Language Processing: Jason Eisner
CS 466: Information Retrieval and Web Agents: David Yarowsky
CS 468/668: Machine Translation: Philipp Koehn
CS 865: Selected Topics in Natural Language Processing: Jason Eisner
CS 866: Selected Topics in Meaning, Translation and Generation of Text: Kyle Rawlins; Benjamin Van Durme
CS 868: Selected Topics in Machine Translation: Philipp Koehn
ECE 315/515: Information Processing of Audio and Visual Signals: Hynek Hermansky
ECE 666: Information Extraction from Speech and Text: Sanjeev Khudanpur
ECE 680: Speech and Auditory Processing by Humans and Machines: Hynek Hermansky
ECE 735: Sensory Information Processing: Andreas Andreou
Beyond these ML-oriented courses, language and speech researchers also need domain knowledge. A full spectrum of graduate linguistics courses is offered by JHU’s #1-ranked Cognitive Science Department. It is also worth considering how humans solve language learning and linguistic inference problems, via JHU’s various courses on language acquisition, psycholinguistics, and neurolinguistics.

AMS 493/693: Mathematical Image Analysis: Nicolas Charon
APL 443: Real-Time Computer Vision: Philippe Burlina; Daniel DeMenthon
CS 361/461: Computer Vision: Gregory Hager
CS 462: Advanced Topics in Computer Vision: Rene Vidal
CS 485: Probabilistic Models of the Visual Cortex: Alan Yuille
CogSci 814: Research Seminar in Computer Vision: Alan Yuille

Vision research at JHU spans many application areas, including robotics, computer-assisted surgery, biomedical imaging instrumentation, medical imaging systems, optics, visual perception, and vision neuroscience.

If you would like your course added to this list, please email [email protected]