Core Machine Learning



Mathematical, Statistical, and Computational Background

The following courses are good sources for foundational background that is often needed in machine learning.

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. See the course search engine.

Applied Machine Learning


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. A full list of biostatistics courses is here.


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.


Language and Speech

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.


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. For more vision-specific courses in these areas, see the Vision Lab list. See also the list of imaging related courses at the Hopkins Imaging Initiative.