Coursework
Online Courses
- Convolutional Neural Networks for Visual Recognition (Fei-Fei Li et al.)
- Machine Learning (Andrew Ng)
- AWS Certified Developer Associate Course (Amazon)
Essential Courses…
…that I have taken/am currently taking† at the University of Toronto.
‡: graduate level.
Compute Science
- †‡CSC413/2516: Neural Networks and Deep Learning (Jimmy Ba, Bo Wang)
- †CSC369: Operating Systems (Karen Reid)
- CSC343: Introduction to Databases (Diane Horton)
- †CSC309: Programming on the Web (Mark Kazakevich)
- CSC265: Enriched Data Structures and Analysis (Faith Ellen)
- CSC258: Computer Organization (Rabia Bakhteri)
- CSC240: Enriched Theory of Computation (Faith Ellen, James Cook)
- CSC209: C and Systems Programming (Karen Reid)
- CSC207: Software Design (Lindsey Shorser)
Statistics
- †STA492: Seminar in Statistical Science (Deep Learning Research) (David Brenner)
- †STA490: Statistical Consultation, Communication, and Collaboration (Liza Bolton, Michael Moon)
- ‡STA414/2104: Probabilistic Machine Learning (Murat Erdogdu)
- STA355: Theory of Statistical Practice (Keith Knight)
- STA347: Probability (Mohammad Khan)
- STA314: Methods for Machine Learning (Daniel Simpson)
- STA304: Surveys, Sampling and Observational Data (Samantha Jo-Caetano)
- STA303: Methods of Data Analysis II (George Stefan)
- STA302/1001: Methods of Data Analysis I / Applied Regression Analysis (Shivon Sue-Chee)
- STA261: Probability and Statistics II / Statistical Inference (Robert Zimmerman)
- STA257: Probability and Statistics I (Katherine Daignault)
Mathematics
- APM462: Nonlinear Optimization (Jonathan Korman)
- †APM306: Mathematics and Law (Nicholas Derzko)
- MAT337: Introduction to Real Analysis (Tomas Kojar)
- MAT237: Multivariable Calculus (Regina Rotman)
- MAT224: Linear Algebra II (Sean Uppal)
- MAT102: Introduction to Proofs (Timothy Yusun)