Select Topics in Machine Learning: Signal Processing, Optimization, and Deep-Learning Methods
Independent study, Fall 2026, The Cooper Union (Dept. of Electrical and Computer Engineering). One hour of lecture per week (time TBA), followed by assigned readings, problem sets, and a final project.
Instructor: Nikola Janjušević
Email: nikola dot janjusevic at cooper dot edu
Office Hours: by appointment
References:
Prince, J. L., and J. M. Links, Medical Imaging Signals and Systems, 2nd ed., Pearson, 2014.
Fessler, J. A., Image Reconstruction: Algorithms and Analysis, book draft.
Boyd, S., and L. Vandenberghe, Convex Optimization, Cambridge University Press, 2004.
Parikh, N., and S. Boyd, Proximal Algorithms, Foundations and Trends in Optimization, 2014.
Chambolle, A., and T. Pock, An introduction to continuous optimization for imaging, Acta Numerica, 2016. (open access)
Ongie, G., et al., Deep learning techniques for inverse problems in imaging, IEEE J. Sel. Areas Inf. Theory, 2020. (arXiv)
Monga, V., Y. Li, and Y. C. Eldar, Algorithm unrolling, IEEE Signal Processing Magazine, 2021. (arXiv)
Goodfellow, I., Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2016.
Software: Your choice of programming language. Python with PyTorch, or Julia with Lux.jl / Flux.jl, are recommended.
(Week 2) Homework 1, due start of class Week 4. Data: hela-cells.png, modl_brain_slice.h5
Study notes (2-3 pages) submitted with each problem set.
Assigned Week 9, proposal due Week 11, presentations Week 15.
