Colin Bellinger

Colin Bellinger

Associate Professor, School of Electrical Engineering and Computer Science, University of Ottawa

Teaching

CSI 5340 Introduction to Deep Learning and Reinforcement Learning

Graduate course, University of Ottawa, Computer Science, 2026

Fundamental of machine learning; multi-layer perceptron, universal approximation theorem, back-propagation; convolutional networks, recurrent neural networks, variational auto-encoder, generative adversarial networks; components and techniques in deep learning; Markov Decision Process; Bellman equation, policy iteration, value iteration, Monte-Carlo learning, temporal difference methods, Q-learning, SARSA, applications. This course is equivalent to COMP 5340 at Carleton University.

ITI 1121 Introduction to Computing II

Undergraduate course, University of Ottawa, Information Technology, 2026

Object-oriented programming. Abstraction principles: information hiding and encapsulation. Linked lists, stacks, queues, binary search trees. Iterative and recursive processing of data structures. Virtual machines.

ISAP3001A Prncples or Apps in Data Analysis

Undergraduate course, Carleton University, Integrated Science, 2023

Data analysis strategies to tackle real-world, wicked problems. Includes a hands-on applied environmental data science project with a variety of partners. Topics include: obtaining and working with data, exploring causal relationships, data ethics, communicating data, and moving from data to information to action.

ISAP3001A Prncples or Apps in Data Analysis

Undergraduate course, Carleton University, Integrated Science, 2022

Data analysis strategies to tackle real-world, wicked problems. Includes a hands-on applied environmental data science project with a variety of partners. Topics include: obtaining and working with data, exploring causal relationships, data ethics, communicating data, and moving from data to information to action.

ISAP3001A Prncples or Apps in Data Analysis

Undergraduate course, Carleton University, Integrated Science, 2021

Data analysis strategies to tackle real-world, wicked problems. Includes a hands-on applied environmental data science project with a variety of partners. Topics include: obtaining and working with data, exploring causal relationships, data ethics, communicating data, and moving from data to information to action.