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Machine Learning and Artificial Intelligence

This sections documents some examples of my past work in the field of machine learning and artificial intelligence.

CMPUT 466 - Machine Learning

This elective offered an introduction into the foundational mathematics that govern basic regression and classification models. Topics included:

• Linear Regression
  • Mean Squared error
  • Gradient Descent
  • Closed-form solution
  • Maximum Likelihood Estimation
  • Maximum a Posteriori Inference
• Linear Classification
  • Logistic Regression
  • Softmax Regression
  • Naive Bayes
• Neural Networks

The course included a “mini-project” in which we had to apply several machine learning models to a simple problem of our choosing. I chose to generate classification models for the Titanic passengers dataset, the code for and results of which can be seen in this notebook.

University of Alberta Centre for Smart Transportation

See my work experience page.

Udacity - Machine Learning Nanodegree

To prevent myself from going insane while stuck in my house over the summer of 2020, I decided the best remedy for that would be to complete a crash course in machine learning. This course offered an introduction to supervised, unsupervised, and deep learning. Each section included small projects such as landmark classification and flower classification.

Throughout the course, I gained experience working with key libraries in Python. I used NumPy and Pandas for data processing, and we used Scikit-Learn and PyTorch machine learning libraries for examples and projects. We also primarily worked in Jupyter notebooks, some of which I have linked below, along with the course certificate.

Image Classifier Project notebook
Supervised Learning Project notebook