Kieran Pattison

Running tutorials/office hours to teach and solidify machine learning concepts. We cover supervised and unsupervised models such as random forests, KNN, K-means, linear/logistic regression, PCA, neural networks and convolutional neural networks as well as probabilistic machine learning concepts like MLE and MAP.

Received an NSERC Undergraduate Student Research Award working on developing optimization algorithms for data science and machine learning.

Assisting in lectures, holding office hours, and leading tutorials. Teaching the fundamentals of Divide and Conquer, Dynamic Programming, Greedy Algorithms and NP Completeness.

Responsible for answering questions on course material in office hour blocks, regarding C, Java Programming, Assembly and Async Programming

Developing with Jupyter, Pandas and scikit-learn to train a model to generate in-line code-review style comments for pull requests using open source repositories.