Henry Rutgers
Professor Computing Science
Architect of Analogies and Wizard of Workarounds
Education:
- Ph.D. in Computer Science, University of British Columbia
- M.Sc. in Computer Engineering, University of Alberta
- B.Sc. in Mathematics and Computer Science, Simon Fraser University
Research Interests:
- Artificial Intelligence and Machine Learning
- Natural Language Processing
- Computational Neuroscience
Courses
CS3201: Machine Learning and Data Mining
Introduction to the theory and practice of machine learning and data mining techniques. Topics include supervised and unsupervised learning, classification, regression, clustering, and evaluation methods
CS3440: Introduction to Artificial Intelligence
An introduction to fundamental concepts and techniques in Artificial Intelligence (AI), including search algorithms, knowledge representation, reasoning, machine learning, and AI applications.
CS4301: Natural Language Processing
Introduction to principles and algorithms used to analyze and process natural language text. Topics include text preprocessing, lexical analysis, syntactic parsing, semantic analysis, information retrieval, and sentiment analysis.
CS4570 Advanced Topics in Machine Learning
Advanced course covering cutting-edge research topics and techniques in machine learning. Topics may include deep learning, neural network architectures, reinforcement learning, generative models, and probabilistic graphical models.
CS5501: Computational Neuroscience
Interdisciplinary course introducing principles and methods of computational neuroscience. Topics include neural modeling, neuronal dynamics, synaptic plasticity, neural networks, and brain-inspired algorithms.
CS5601: Advanced Algorithms and Data Structures
Advanced course covering algorithms and data structures used in computer science and engineering applications. Topics include graph algorithms, dynamic programming, randomized algorithms, computational geometry, and parallel algorithms.