Below is an approximate list of ML courses offered at JHU. Most are open to both graduate students and upper-level undergrads. Students are encouraged to take courses outside of their home department. Note that the formal ML curriculum is supplemented by several very active speaker series and reading groups (listed on our home page), as well as special-topics seminar courses. Prospective students can apply here.
Core Machine Learning
Introductory
- AMS 426: Data Mining Bruno Jedynak
- AMS 437: Statistical Learning with Applications Donald Geman
- BME 491/691: Learning Theory I Reza Shadmehr
- BioStats 646-649: Essentials of Probability and Statistical Inference I-IV Michael Rosenblum
- BioStats 776: Statistical Computing Hongkai Ji
- CS 475: Machine Learning Mark Dredze
- CogSci 371/671: Formal Methods in Cognitive Science: Inference Paul Smolensky
- CogSci 372/672: Formal Methods in Cognitive Science: Neural Networks Paul Smolensky
- ECE 447: Introduction to Information Theory and Coding Damianos Karakos
Advanced
- AMS 640: Machine Learning Laurent Younes
- AMS 643: Graphical Models Laurent Younes
- AMS 730: Statistical Pattern Recognition Carey Priebe
- AMS 735: Topics in Statistical Pattern Recognition Carey Priebe
- BME 692: Learning Theory II Rene Vidal
- 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 in Complex Domains (How to Become a Data Ninja) Suchi Saria
- CS 775: Current Topics in Machine Learning Mark Dredze
- ECE 674: Information Theoretic Methods in Statistics Sanjeev Khudanpur
Mathematical, Statistical, and Computational Background
The following courses are good sources for foundational background that is often needed in machine learning.
- AMS 420: Introduction to Probability John Wierman
- AMS 430: Introduction to Statistics Daniel Naiman
- AMS 433: Monte Carlo Methods Nam Lee
- AMS 661: Foundations of Optimization Treven Wall
- AMS 662: Optimization Algorithms Daniel Robinson
- AMS 663: Stochastic Search and Optimization James Spall
- AMS 666: Combinatorial Optimization Rico Zenklusen
- AMS 692: Matrix Analysis and Linear Algebra Youngmi Hur
- AMS 723: Markov Chains Jim Fill
- 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 325/425: Declarative Methods Jason Eisner
- CS 420: Parallel Programming Randal Burns
- CS 435: Artificial Intelligence Benjamin Mitchell
- CS 464/664: Randomized Algorithms Rao Kosaraju
- ECE 651: Random Signal Analysis Sanjeev Khudanpur
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
Biology
- AMS 635: 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 416: Current Topics in Protein Structure Prediction Jeffrey Gray
- ECE 610: Computational Genomics M Ermolaeva
- Med 707: Bioinformatics Jonathan Pevsner
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
- 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.
Economics
- Econ 613: Bounded Rationality J Quiggin
- Econ 614: Mathematical Economics Ali Khan
- Econ 617: Multi-Agent Numerical Methods P Jeziorski
- Econ 617: Topics in Mathematical Economics Ali Khan
- Econ 636: Statistical Inference Jonathan Wright
Language and Speech
- CS 465: Natural Language Processing Jason Eisner
- CS 466: Information Retrieval and Web Agents David Yarowsky
- CS 468: Machine Translation Chris Callison-Burch; Adam Lopez; Matt Post
- CS 765: Selected Topics in Natural Language Processing Jason Eisner
- CS 766: Selected Topics in Meaning, Translation, and Generation of Text Ben Van Durme; Chris Callison-Burch
- 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.
Vision
- AMS 431: Statistical Methods in Imaging Bruno Jedynak
- AMS 493: Mathematical Image Analysis Laurent Younes
- 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
- ECE 414: Image Processing and Analysis John Goutsias
- ECE 433: Medical Image Analysis Jerry Prince
- ECE 745: Seminar: Medical Image Analysis Jerry Prince
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.