Teaching
Course staff (TA) and mentoring.
COMP 551 — Applied Machine Learning
Selected topics in ML and data mining, including clustering, neural networks, SVMs, and decision trees. Methods include feature selection & dimensionality reduction, error estimation & empirical validation, algorithm design & parallelization, and handling of large datasets. Emphasis on best practices for production systems.
COMP 550 — Natural Language Processing
An introduction to computational modelling of natural language: morphology, language modelling, syntactic parsing, lexical & compositional semantics, discourse, and applications such as summarization, machine translation, and speech. ML techniques for NLP.
COMP 424 — Artificial Intelligence
Introduction to search methods; knowledge representation using logic and probability; planning and decision-making under uncertainty; and an introduction to machine learning.
COMP 302 — Programming Languages and Paradigms
Programming language design issues and paradigms: binding and scoping, parameter passing, lambda abstraction, data abstraction, and type checking. Functional and logic programming exposure.
Mentoring
- Mentor — AI4Good Lab
- Mentor — Women in AI