Technical Favorites
MKTG 7760 — Applied Probability Models in Marketing
One of my favorite courses at Penn, taught by one of the best professors I’ve had. A mix of technical and conceptual. We rebuilt probability models from scratch, worked in Bayesian thinking and updating, and rethought retention and customer profiles. The course is honest about machine learning—what it lacks and what it adds. We looked at models like the Dirichlet that show up in research. Time consuming, but approachable math and worth it. If you like quantitative work and aren’t afraid of math, take it.
BayesianDirichletCustomer Models
CIS 2400 — Computer Systems
This course shows how a computer actually works bits, ISA, compilers, memory, pipelines. We wrote assembly and C, built a C compiler that ties into LC-4 machine code, and moved from gates to higher abstractions. Great class if you want to know what’s hidden when you write higher-level code.
CAssemblyArchitecture
CIS 5200 — Machine Learning
Math heavy and theoretical. Less about coding outputs, more about why algorithms work. The professor emphasizes intuition and proofs: kernels, regularization theory beyond just lambda, SVMs from the ground up. He makes a lot of the theory super approachable though, and it makes it easy to digest. If you're interested in doing more ML courses, I would take this instead of CIS5190: Applied Machine Learning.
KernelsGeneralizationSVM
FNCE 2050 — Investment Management
A good mix of concept and math. Very research-based. The professor brings in his own work and others’. Clear about the strengths and weaknesses of different models and the assumptions people use in practice. Useful for understanding portfolio construction and attribution.
OptimizationAttribution
STAT 4330 — Stochastic Processes
We covered martingales, Markov chains, proofs, and probability at a deep level. Real analysis ideas showed up in the later part of the course. Difficult but worthwhile. Changed how I think about probability and expectation. Great class if you're willing to sit through it being practically all theoretical.
MartingalesMarkov ChainsReal Analysis