personal review

Ranking the major-relevant classes I’ve taken. I’m biased towards applied math and CS but I tend to stay away from AI/ML stuff because it’s not my cup of tea. Not a definitive ranking, just what I liked learning about.

cs

  1. Compilers (CS 536)
  2. Communication Networks (ECE 537)
  3. Theory & Design of Programming Languages (CS 538)
  4. Machine Organization & Programming (CS 354)
  5. Parallel & Throughput-Optimized Programming (CS 639)
  6. Algorithms (CS 577)
  7. Artificial Intelligence (CS 540)

math

  1. Combinatorics (MATH 475) + Stochastic Processes (MATH 632)
  2. Probability Theory (MATH 431)
  3. Integer Optimization (MATH 728)
  4. Linear Optimization (MATH 525)
  5. Linear Algebra (MATH 341) + Analysis (MATH 521)

spring 2024

✏️ CS 728: (Graduate) Integer Optimization
✏️ CS 536: Introduction to Compilers
✏️ CS 639: Parallel & Throughput-Optimized Programming
✏️ ECON 301: Intermediate Economic Theory
✏️ ASIAN 357: Japanese Ghost Stories
✏️ MUSIC 113: Music in Performance
💼 System Administrator Intern @ Morgridge Institute of Research
💼 Peer Mentor @ CS 538: Theory and Design of Programming Languages

fall 2023

✏️ CS 538: Introduction to Theory and Design of Programming Languages
✏️ MATH 525: Introduction to Linear Optimization
✏️ MATH 632 (Honors): Introduction to Stochastic Processes
✏️ ECON 111 (Honors Accelerated): Principles of Economics
✏️ HISTORY 143: History of Race and Inequality in Urban America
✏️ MUSIC 113: Music in Performance
💼 System Administrator Intern @ Morgridge Institute of Research

summer 2023 (self-studied)

✏️ 6.S191: Introduction to Deep Learning MIT
✏️ CS 544: Introduction to Big Data Systems
💼 Software Engineer Intern @ Mandli Communications

spring 2023

✏️ CS 354: Machine Organization and Programming
✏️ CS 540: Introduction to Artificial Intelligence
✏️ CS 577: Introduction to Algorithms
✏️ MATH 431: Introduction to the Theory of Probability
✏️ MATH 521 (Honors): Analysis I
🔬 Research project: Training and optimizing image generation models on custom datasets.

fall 2022

✏️ CS 252: Introduction to Computer Engineering
✏️ CS 475: Introduction to Combinatorics
✏️ ECE 537: Communication Networks
✏️ PHYSICS 201 (Honors): General Physics
✏️ MSE 299: Independent Study — Machine Learning for Engineering Research. Learned basic ML workflow like cleaning data, training models, and optimization.

summer 2022 (self-studied)

✏️ CS 220: Data Programming I
✏️ CS 61A: Structure and Interpretation of Computer Programs UC-Berkeley

previous

✏️ CS 300: Programming II (SU ‘20)
✏️ CS 400: Programming III (FA ‘20)
✏️ ECE 203: Signals, Information, and Computation (SU ‘21)
✏️ MATH 20804232: Calculus & Analytic Geometry 2 (SU ‘21) Madison College
✏️ MATH 234: Calculus - Functions of Several Variables (FA ‘21)
✏️ MATH 341 (Honors Accelerated): Linear Algebra (SP ‘22)