Projects in Programming

My Projects in Programming class taught me useful skills or tools to work with APIs and the data that we can extract from them through weekly labs. This project based class culminated in fully functioning web app as our final project using all the skills we learned throughout the semester.

Machine Learning Course

I took a Machine Learning course in NYU Shanghai and these are the projects I had to build in order to learn certain important concepts. I implemented simple ML models such as Support Vector Machines (SVM) and Linear Regression in Numpy. Then I utilized PyTorch to build the more complicated Deep Learning models such as CNN's, RNN's, as well as simple perceptron algorithms. We also learned how to appropriately optimize these models to get higher training, validation, and test accuracy.

Regression and Multivariate Data Analysis

In my Regression and Multivariate Data Analysis class I wrote five seperate research papers for which I created 5 datasets of differing data types and then used appropriate regression models to interpret underlying trends in the respective data sets. The class was also heavily focused on how to interpret the regression diagnostics for the purpose of model selection and to verify that the assumptions of least squares regression hold.

Decision Models & Analytics

I took a Decision Models and Analytics class where I built models in Excel to solve important business problems such as profit optimization or decision making under uncertainty. Used Simplex Solver to solve linear programming optimization models & GRG Nonlinear or Evolutionary Solver to solve non-linear optimization models. Then introduced Crystal Ball to for simulation modeling. Simulation models for normal, log-normal, bernoulli etc. distributions. Included constraints and optimization for certain variables, i.e. simulating profit distribution while minimizing costs under demand uncertainty.