Here are a list of courses I have taken (at UMich, as well as online):

Fall 2022

• MATH 217 Proof-based Linear Algebra

• HISTORY 293 Ancient Egypt and its World

• EECS 376 Foundations of Computer Science

• TCHNCLCM 300 Technical Communication for EE&CE

• Robotics Research: ML, Control Engineering (MPC), Bayesian Inference, Optimization

Summer 2022

• WCC Multivariate and Vector Calculus

• Codepath Advanced Software Engineering

• Stanford Online Social and Economic Networks: Models and Analysis

• Texts on convex optimization, analysis, functional programming and cryptography

Winter 2022

• EECS 281 Data Structures and Algorithms

• ENGR 100 Fundamentals of Operations Research and Optimization

• IOE 265 Probability and Statistics + R

• PHYS 240/1 General Physics II + Lab

• Edtech product and solar racing strategy research

Fall 2021

• EECS 280 Programming and Introduction to Data Structures

• HISTORY 318 Europe in the Era of Total War

• EECS 203 265 Discrete Mathematics

• CHEM 125/126/130 General Chemistry + Lab

Prior Credit

• MIT 6.00.1x Introduction to Computer Science Programming in Python

• MATH 116, Calculus II [AP Calculus BC]

• MIT 6.00.2x Introduction to Computational Thinking and Data Science

• MATH 115, Calculus I [AP Calculus BC]

• PHYS 140/141, General Physics I [AP Physics C: Mechanics]

MOOCs

• Machine Learning as taught by Andrew Ng, Stanford University on Coursera.
• The Deep Learning Specialization by deeplearning.ai on Coursera. Courses include:
  • Neural Networks and Deep Learning
  • Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
  • Structuring Machine Learning Projects
  • Convolutional Neural Networks
  • Sequence Models
• Introduction to Mathematical Thinking by Stanford University on Coursera.
• Mathematics for Machine Learning: Linear Algebra by Imperial College London on Coursera.