Liam Black Rohrer

Course Work at the University of Auckland

Machine Learning (COMPSCI 361)

Model Analysis of Naive Bayes, k Nearest Neighbors, Support Vector Machines, and Artificial Neural Networks for Document CLassification
Classifying Document Types with Naive Bayes
Building and Assessing a Decision Tree Based ML Classifier

Applied Time Series Analysis (STATS 326)

Forecasting with ETS and ARIMA
STL Decomposition and Time Series Linear Regression

Applied Multivariate Analysis (STATS 302)

Principal Component and Linear Descriminent Analysis
Cannonical Correlation Analysis and MANOVA
Metric Multidimentional Scaling

Statistical Modelling (STATS 330)

GLMs with Count Data
Logistic Regression with GLMs and GAMs
More Logistic Regression, GLMs, and GAMs
Predictive Modelling for Lobster Survival Rates

Optimisation and Data-driven Decision Making (STATS 255)

Linear Programming
Decision Problems
Project and Inventory Management
Simulated Inventory and Queueing Systems

Discrete Structures in Mathematics and Computer Science (COMPSCI 225)

Boolean Logic and Basic Arithmetic Proofs
Proof by Cases, Induction, and Direct Proofs
Proofs using Sets, Relations, and Graphs
Proofs Using Set Theory

Data Technologies (STATS 220)

STATS 220 Github Repository
Dynamic Report

Digital Findamentals (PHYSICS 140)

Flip-Flops Used in Digital Logic Design
Binary Counters and Shift Registers

Additional UOA Work Links

Algorithms and Data Structures (COMPSCI 220) Github Repository
Applied Algorithms (COMPSCI 320) Github Repository

Other Work I've Done

Previous Work Experience